notebooks/Lecture1_IntroWithACaseStudy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cvIcZ7dYZ3A4"
   },
   "source": [
    "## Basic flow in an examplary MIR - classification research task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xQSslF12Z3A_"
   },
   "source": [
    "This notebook is designed to demonstrate basic/common flow of processes in an MIR classification task: \"Automatic instrument recognition\". The content should be considered as educational material rather than research material. Various segments of it aim to raise a question for discussion rather than presenting a state-of-the-art solution. Indeed, the process has a problem that we later observe and discuss close to the end, try spotting it yourself while reading the code.\n",
    "![MIR classification task basic flow](https://raw.githubusercontent.com/MTG/MIRCourse/master/notebooks/figures/MIRresearchOverview_1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SOsShVeHZ3BG"
   },
   "source": [
    "**In-class discussion topics:**\n",
    "* Defining the problem and the context, data space, reqirements, specifications <-> Literature review\n",
    "* Curating a representative research dataset (available datasets, common/standard representation formats)\n",
    "* Designing the flow, algorithms/processes/methods and implementation\n",
    "* Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "C98uNhZyT5Vh"
   },
   "source": [
    "**Reading list:** https://sites.google.com/site/mirspring2018/my-reading-list\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pD2Du8odZ3BJ"
   },
   "source": [
    "## A classification task: instrument recognition\n",
    "\n",
    "* Let's pick one of the standard MIR tasks: automatic instrument recognition, and limit our context as monophonic isolated notes recordings.\n",
    "* The task is a classification task; hence, our first aim is gather a set of isolated note recordings for various instruments with instrument labels. We will be using (downloading) samples from an existing recording collection ([the IOWA:MIS dataset](http://theremin.music.uiowa.edu/MIS.html))\n",
    "* Our algorithmic design will be based on the standard flow of feature extraction followed by feeding the data to a classifier  (see figure below)\n",
    "* At the end, we will use standard evaluation measures of automatic classification to test our approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sliVDC3GYPcT"
   },
   "outputs": [],
   "source": [
    "#If not installed, install Essentia. \n",
    "# This cell is for running the notebook in Colab\n",
    "import importlib.util\n",
    "if importlib.util.find_spec('essentia') is None:\n",
    "  !pip install essentia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8RP7NclOZ3BO"
   },
   "outputs": [],
   "source": [
    "#Basic imports\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import essentia.standard as ess\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EhDMnGtEZ3BW"
   },
   "source": [
    "## Collecting raw data and investigating its nature\n",
    "\n",
    "### Downloading dataset files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "i_y1fdZZZ3BZ",
    "outputId": "5b8fa273-eace-458d-ed45-12734b536465"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading file for  flute\n",
      "Data downloaded and unzipped to:  instrument/flute\n",
      "Downloading file for  violin\n",
      "Data downloaded and unzipped to:  instrument/violin\n",
      "Downloading file for  vibraphone\n",
      "Data downloaded and unzipped to:  instrument/vibraphone\n",
      "Downloading file for  clarinet\n",
      "Data downloaded and unzipped to:  instrument/clarinet\n"
     ]
    }
   ],
   "source": [
    "#This cell downloads a subset of the UIOWA:MIS dataset\n",
    "import urllib.request\n",
    "import zipfile\n",
    "import os, sys,shutil\n",
    "\n",
    "#Links for some of files in the UIOWA:MIS dataset\n",
    "links = {'flute':'http://theremin.music.uiowa.edu/sound%20files/MIS/Woodwinds/flute/flute.nonvib.ff.zip',\n",
    "         'violin':'http://theremin.music.uiowa.edu/sound%20files/MIS/Strings/violin2012/Violin.arco.mono.1644.1.zip',\n",
    "         'vibraphone': 'http://theremin.music.uiowa.edu/sound%20files/MIS%20Pitches%20-%202014/Percussion/Vibraphone/Vibraphone.dampen.ff.stereo.zip',\n",
    "         'clarinet':'http://theremin.music.uiowa.edu/sound%20files/MIS/Woodwinds/Ebclarinet/EbClar.ff.zip'}\n",
    "\n",
    "main_data_dir = 'instrument'\n",
    "if not os.path.exists(main_data_dir):#creating the directory if not exist\n",
    "    os.mkdir(main_data_dir)\n",
    "for instrument, url in links.items():\n",
    "    print('Downloading file for ', instrument)\n",
    "    targetDir = os.path.join(main_data_dir,instrument)\n",
    "    if not os.path.exists(targetDir):#creating the director\n",
    "        os.mkdir(targetDir)\n",
    "    filename = url.split('/')[-1]\n",
    "    urllib.request.urlretrieve(url,filename)\n",
    "    #Unzipping to a specific folder\n",
    "    zip_ref = zipfile.ZipFile(filename, 'r')\n",
    "    zip_ref.extractall(targetDir)\n",
    "    zip_ref.close()\n",
    "    os.remove(filename)#Removing the zip file\n",
    "    print('Data downloaded and unzipped to: ',targetDir)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iYsfA0CNZ3Bh"
   },
   "source": [
    "**Collect file lists for each instrument**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "AfXV-LblZ3Bj",
    "outputId": "ce56cd00-090d-4774-f57e-f67efae560d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'flute': ['instrument/flute/flute.nonvib.ff.B3B4.aiff',\n",
       "  'instrument/flute/flute.nonvib.ff.C7Db7.aiff',\n",
       "  'instrument/flute/flute.nonvib.ff.C6B6.aiff',\n",
       "  'instrument/flute/flute.nonvib.ff.C5B5.aiff'],\n",
       " 'Violin': ['instrument/violin/Violin.arco.ff.sulA.C6Ab6.mono.aif',\n",
       "  'instrument/violin/Violin.arco.ff.sulE.C6B6.mono.aif',\n",
       "  'instrument/violin/Violin.arco.ff.sulG.G3B3.mono.aif',\n",
       "  'instrument/violin/Violin.arco.ff.sulG.C5D5.mono.aif',\n",
       "  'instrument/violin/Violin.arco.ff.sulE.C7E7.mono.aif',\n",
       "  'instrument/violin/Violin.arco.ff.sulD.D4B4.mono.aif',\n",
       "  'instrument/violin/Violin.arco.ff.sulE.E5B5.mono.aif',\n",
       "  'instrument/violin/Violin.arco.ff.sulD.C6.mono.aif',\n",
       "  'instrument/violin/Violin.arco.ff.sulA.C5B5.mono.aif',\n",
       "  'instrument/violin/Violin.arco.ff.sulD.C5B5.mono.aif',\n",
       "  'instrument/violin/Violin.arco.ff.sulG.C4B4.mono.aif',\n",
       "  'instrument/violin/Violin.arco.ff.sulA.A4B4.mono.aif'],\n",
       " 'EbClar': ['instrument/clarinet/EbClar.ff.C5B5.aiff',\n",
       "  'instrument/clarinet/EbClar.ff.G3B3.aiff',\n",
       "  'instrument/clarinet/EbClar.ff.C4B4.aiff',\n",
       "  'instrument/clarinet/EbClar.ff.C6A6.aiff'],\n",
       " 'Vibraphone': ['instrument/vibraphone/Vibraphone.dampen.ff.Bb5.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Db3.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.G3.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Db5.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.B3.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Ab5.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.D4.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.E6.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Ab4.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Gb3.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Bb4.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Db6.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.B5.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Gb5.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Eb4.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Db4.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Eb6.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Eb3.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.E4.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.G4.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.D5.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Bb3.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.A5.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.E5.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.A3.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.F4.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.C5.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.C6.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.F5.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Gb4.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.C3.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.C4.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.F6.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.F3.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Eb5.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.Ab3.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.A4.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.B4.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.G5.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.D6.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.D3.stereo.aif',\n",
       "  'instrument/vibraphone/Vibraphone.dampen.ff.E3.stereo.aif']}"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_data_dir = 'instrument'\n",
    "inst_files = dict()#dictionary containing for each instrument a list that carries dictionaries\n",
    "for root, dirs, files in os.walk(main_data_dir):\n",
    "    for file in files:\n",
    "        #Let's only keep forte recordings indicated with .ff. in the file name\n",
    "        if (file.endswith('.aif') or file.endswith('.aiff')) and ('.ff.' in file):\n",
    "            file_name = os.path.join(root,file)\n",
    "            instrument = file.split('.')[0]#instrument name is coded in the filename\n",
    "            if(len(instrument) > 0):#avoid MACOS files that start with ._\n",
    "                files_instrument = inst_files.get(instrument)\n",
    "                if files_instrument == None:\n",
    "                    files_instrument = [file_name]\n",
    "                else:\n",
    "                    files_instrument.append(file_name)\n",
    "                inst_files[instrument] = files_instrument\n",
    "\n",
    "#Let's print the list of files\n",
    "inst_files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KFmukYMfZ3Br"
   },
   "source": [
    "Now that we have gather a small research dataset, we can now focus designing the basic flow for our classification task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t-c6KFqRZ3Bu"
   },
   "source": [
    "![Basic flow](https://raw.githubusercontent.com/MTG/MIRCourse/master/notebooks/figures/classification_basicFlow.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oYZZTtYBZ3Bw"
   },
   "source": [
    "### Investigating and preprocessing raw data\n",
    "\n",
    "It is often very useful to first check a few samples from the dataset to decide what preprocessing steps are required. Let's have a look at the waveform of one sample recording for each instrument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "colab_type": "code",
    "id": "Vr2f3y2dZ3By",
    "outputId": "5320d447-0001-4ffd-a393-8c283adc7145"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample waveform plots\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJoAAADSCAYAAAACLaqUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3wUdf4/8Nc7Cb13kBY6AoolgKCiWLGBnmKvZzm98+53X+/0gkoRRLGfBQsqKjZU9BRNICCEXgNIbyEEEmpIIBBCyibv3x87GzabTXZ25jM7s7vv5+PBg93Zmc98tmTmM+/5fN4fYmYIIYQQQgghhBBCCGFWjN0VEEIIIYQQQgghhBCRQQJNQgghhBBCCCGEEEIJCTQJIYQQQgghhBBCCCUk0CSEEEIIIYQQQgghlJBAkxBCCCGEEEIIIYRQQgJNQgghhBBCCCGEEEIJCTSJkCCiXkT0BxGdJKI8InrR7joJIQQRfUhEY3Suy0TUPdjthBDCFxE9SERL7dpeCBFZvNslRHQ5EWWHcN/jieirUO1PhIc4uysgosYzAFKZ+Twi+lzvRtq62cz8vFUVE0JELiKaA2A1M4/1WT4SwEcAOjCzK9hymflxRVUUQkQwIsoE0AZAmdfizwGk6dj2WgDPATgfQBGArQDeYOZZyisqhHA0q9ozQlhFejSJUOkMYIvdlRBCRJ0vANxLROSz/D4AX0ujTAgRAjcxc0Ovf08G2oCIbgPwA4DpADrAHawaC+Ams5UhIrnRLET4saw9Q0SxpmomhB8SaBKWI6IFAIYBeI+ICgDU9nqtStdvz/AUInoMwD0AniGiAiL6VXv9LCL6kYhyiGgPEf0jhG9HCBFefgbQAsClngVE1AzAjQCmE9Hn3kN5iehRIkrXhvjOIqKz/BXqvZ2nizoR/YuIjhDRQSJ6yNq3JYSIAERE7xFRPhFtJ6IrPQsBvAlgIjN/wsz5zFzOzIuY+dFqCnqbiLKI6AQRrSUi72PeeCKaSURfEdEJAA+G4s0JIZQKqj2jvf4sER0lokwiusdr+edE9AERJRPRKQDDiOgGIlqvHUOyiGi81/rx2vXZY0R0QGvn/NunfrWJaLqWJmULESV4bX82ES0kouPaayN86jKFiJK0bVcRUTev13sT0TytXbaDiG43/UmKkJBAk7AcM18BYAmAJ5m5IYASndtNBfA1gFe1O4A3EVEMgF8BbADQHsCVAP6pdS8XQohKmPk0gO8B3O+1+HYA25l5g/e6RHQFgJe119sB2Atghs5dtQXQBO7j0sMApmgNQCGEqM4gALsBtAQwDsBPRNQcQC8AHQHMDKKsNQDOA9AcwDcAfiCiul6vj9TKawp320oIEUaCac9o2sJ9bGkP4AEAU4mol9frdwOYBKARgKUATmllNwVwA4AniOhmnzKHAegB4BoA/yGiq7xeGwF3m6kpgFkA3gMAIqoF97XbXACtAfwdwNc+dbkTwAsAmgFI1+oFImoAYB7cx7TW2nrvE1Gf6j4n4RwSaBLhZgCAVsw8gZlLmDkDwMdwH3iEEMKfLwDc5nXRdb+2zNc9AKYx8zpmLgYwGsBgIorXsY9SABOYuZSZkwEUwH2xKIQQP2t38j3/PL2SjgD4r3bc+A7ADrgv8Fporx/UuwNm/oqZc5nZxcxvAKiDysegFcz8s9Yz6rSC9ySECD297RmPMcxczMyLACTBHZjy+IWZl2nHhCJmXsjMm7TnGwF8C+Ayn/JeYOZTzLwJwGcA7vJ6bSkzJzNzGYAvAfTXll8EoCGAydq12wIAv/ls+z9mXq0N//sa7qA54O6tlcnMn2nHtvUAfgQwKsDnJBxAAk0i3HQGcJZ3gw3As3DnLhBCiCqYeSmAowBu1rpjD4T77pivs+DuxeTZrgBALtx3AwPJ9cmPUAh3w0oIIW5m5qZe/z7Wlu9nZvZaby/cx6Fc7Xk7vTsgon8T0TZtGN5xuHtYtvRaJcvMGxBC2C+I9gwAHGPmU17PPccXj0rHBCIaRESpWmqSfACPo/IxxHcb3/IOeT0uBFBXywd3FoAsZi732bZ9Ddt62k+dAQzyue67B+7eWsLhJNAk7HYKQH3PEyLyPXCwz/MsAHt8GmyNmPl6qysqhAhr0+G+83cvgBRmPuxnnQNwN2oAVHTZbgFgf0hqKISINu21fEweneA+Du2Au71zq55CtHxMz8DdW6EZMzcFkA/Au2zf9pQQIjzpac8AQDOtHePhOb54+B4TvoF7yFtHZm4C4ENUPoYA7iG91ZVXnQMAOmrpT7y31dO2ygKwyOe6ryEzP6FjW2EzCTQJu20A0JeIztO6gY73ef0wgK5ez1cDOElE/yGiekQUS0T9iGhAiOorhAhP0wFcBeBRVN/N/FsAD2nHozoAXgKwipkzQ1NFIUSUaQ3gH0RUi4hGATgbQLLWy+kpAGOI6CEiakxEMUR0CRFN9VNOIwAuADkA4ohoLIDGoXoTQoiQ0tOe8XiBiGprwegb4Z7JsjqNAOQxcxERDYQ7h5OvMURUn4j6AngIwHc66rsK7l5Kz2jHusvhnj1TTw7M3wD0JKL7tG1rEdEAIjpbx7bCZhJoErZi5p0AJgD4HcAuuJPRefsUQB+tu+TP2rjfG+Eeu7sH7u6jn8DdRVwIIfzSgkXLATSA+46dv3V+BzAG7vH/BwF0g+R/E0KY9yu5Z8/1/PuftnwV3Il1j8Kd/PY2Zs4FAGaeCeAOAH+Gu0fAYQAvAvjFT/kpAOYA2An3kJQiyFA5ISKSnvaM5hCAY3AfP74G8Dgzb69h/b8CmEBEJwGMhTvxuK9FcCfrng/gdWaeq6O+JXAHlq6D+1j3PoD7A9TFs+1JuBOP36m9j0MAXoE7B51wOKo8NFwIIYQQQgghhBAC0CZF2QOglk8+SiGqJT2ahBBCCCGEEEIIIYQSEmgSQgghhBBCCCGEEErI0DkhhBBCCCGEEEIIoYT0aBJCCCGEEEIIIYQQSkigSQghhBBCCCGEEEIoEWd3BYxo2bIlx8fH210NIYQJa9euPcrMreyuhxlyLBIi/MmxSAjhFOF+PJJjkRCRQcWxKCwDTfHx8UhLS7O7GkIIE4hor911MEuORUKEPzkWCSGcItyPR3IsEiIyqDgWydA5IYQQQgghhBBCCKGEBJqEEEIIIYQQIooQ0XAi2kFE6USU6Of1TkSUSkTriWgjEV1vRz2FEOFJAk1CCCGEEEIIESWIKBbAFADXAegD4C4i6uOz2vMAvmfm8wHcCeD90NZSCBHOJNAkhBBCCCGEENFjIIB0Zs5g5hIAMwCM9FmHATTWHjcBcCCE9RNChDkJNAkhdFuyKweLdubYXQ1ho0P5RXjws9VgZrurIoQQQpiycMcRvJ6yo8Z1SsvKI/Gc1x5AltfzbG2Zt/EA7iWibADJAP7uryAieoyI0ogoLScnuDbiF8szkX2sMKhthBDhQQJNQohqucrKEZ+YhGdmbgAA3PfpajwwbbXNtRJ2uujl+Vi4Iwcz1mQFXlkIIYRwgOOFJVi371ilZY9NT8ODn63Be6npKC/3H0jKyitEj+dmY/RPm1BUWlaxvLycMSlpK+ITkyotjzB3AficmTsAuB7Al0RU5dqRmacycwIzJ7RqpX829GOnSjBu1hbc+8kqdTUWQjiGBJqEiBKfLMnA2r3HAq/oZeEO952p79OykfjjxorlG7OPK62bCD+5BcV2V0EIIYTQ5bwJ8/Cn95djd04BAKDYVYa5Ww9XvP73Gev9bnfpq6kAgBlrstB7zBx8vyYLp4pdeDVlBz5esgcA0HvMHItrb4n9ADp6Pe+gLfP2MIDvAYCZVwCoC6ClqgqUa73EThS5VBUphHAQCTQJEaE+XpyBIS/Pr3j+YtI23PrBchSW6D+hu8rLKx5792AZ8d4ynC6J2Dt4QgghhIhAT33v7qH99A8bKy1P2nhQ1/bP/LgRfcel4MNFuystj09MwjyvwFUYWAOgBxF1IaLacCf7nuWzzj4AVwIAEZ0Nd6BJ8icIIXSRQJMQEWpS8jYcyC/CrA0HMH/bmcbPBwt317BVZfmnS6t97URR9a8JIYQQQjjNhix3j+xZGwLntb7v0+CGdD06Pc1QnezAzC4ATwJIAbAN7tnlthDRBCIaoa32LwCPEtEGAN8CeJAjMFmVEMIacXZXQAhhrX98W7k7+LsL0vHU1T1BRAG3/WL53mpfu/HdpbjhnHYYP6Kv6ToKIYQQQljBNzait0f2kl1Hg95XWTkjNiZw+8oJmDkZ7iTf3svGej3eCuDiUNdLCBEZpEeTEFHoi+WZutbbevBEta/lnCzG5zrLEcKoTdn52H/8tN3VEEKIiHe6pAzMDGbGQ5+txq8bDuCPrPDPyTh5zvZKz88ea11OpSMniywrWwghwon0aBIiAgXq2Tx9xV48eHGXENVGCONuem8pACBz8g0210QIISLX16v24rn/bQYATLqlH1J35CBVmxDkoYvjcXG3lriqTxs7q2jYR4sygt7GkzQ8WD+t24+/DetuaFshhIgk0qNJiAj01ap9Nb6ecfSUsn2lHzmprCwhRHiYviITF700P+B6QgjnW7IrpyLIBKDSYwD4bFkmHpmehvjEpKiZCGRfbqGh7X5al624JkIIEZ4k0CREBBrz8+aA6wTq9fTjWn2NpaUGchgIIcLb2F+24NAJGSIiRCS479PVutc9e+wclJdHfj7oLi0bGNpud466G3nRQvKLCxGZJNAkRJQa/t8lNb7+rx826Cpn0U7nzHRLRMOJaAcRpRNRop/XhxLROiJyEdFtPq89QES7tH8PhK7WQgghhD1cZeVBb7MnN3yCKUZnyI3RMWGKMEfPpDRCiPAlgSYhotSOw2qGvHlyONiNiGIBTAFwHYA+AO4ioj4+q+0D8CCAb3y2bQ5gHIBBAAYCGEdEzayusxBCCGGn/i/MDXqbRQ457+uRX2gs0CSEEMIcCTQJISLFQADpzJzBzCUAZgAY6b0CM2cy80YAvrdwrwUwj5nzmPkYgHkAhoei0uFKeroLIUT4O2Ug59IehXkerbYs3djw/uW7JS2AEEKYIYEmIUSkaA8gy+t5trZM6bZE9BgRpRFRWk5O+NzVFcIKJa7gh91EOx1DfJ8ioq1EtJGI5hNRZzvqKUR1vly51+4qBJR+pADxiUlI/GmToe2zjhlLBi6EEMJNAk1CCBEEZp7KzAnMnNCqVSu7q2ObUHRoOlXsCsFehBk7Dsmsk8HQOcR3PYAEZj4XwEwAr4a2lkKEv6veXGRqe+m1az1JAi5EZJNAkxBRbMbqfX6X3/7hihDXRIn9ADp6Pe+gLbN6W2GRzfvz7a6CaVNS02UIhvCmZ4hvKjN7ulOshPt4JITQ6VC+zIgZTiQpuBCRSQJNQkSx6rqUr87MC3FNlFgDoAcRdSGi2gDuBDBL57YpAK4homZaEvBrtGXCRr9sOGB3FUx7LWUH7v54ld3VEM4R7BDfhwHMtrRGQkSYH9dlmy5D+tqEHjNLLychIogEmoQQEYGZXQCehDtAtA3A98y8hYgmENEIACCiAUSUDWAUgI+IaIu2bR6AiXAHq9YAmKAtEzb6ZpX/HnfCOVguxyxDRPcCSADwWjWvS744Ifx4LWWH3VUQQfAEl7qMTsbIKctsro0QQpU4uysghBCqMHMygGSfZWO9Hq9BNcNQmHkagGmWVjCAy19LxenSMqx69io7qyGEsI6uYbpEdBWA5wBcxszF/gpi5qkApgJAQkKCRPyEUCgaOtYQ0XAAbwOIBfAJM0/2s87tAMbD3clrAzPfrXD/VZZtzA7/IfNCCDcJNAkhhENk5sosN0JEuIohvnAHmO4EUOnCjYjOB/ARgOHMfCT0VRRCRDqviQmuhnsI7xoimsXMW73W6QFgNICLmfkYEbW2p7ZCiHCkZOicjql6hxLROiJyEdFtPq89QES7tH8PqKiPEEIIa0nqzsCiIddEFLxFpfQM8YV7qFxDAD8Q0R9EpDfXnBBCkSgYFhxwYgIAjwKYwszHAEAC30KIYJju0aQnIg5gH4AHAfzbZ9vmAMbBnYOAAazVtj1mtl5CCCGEnSQII/zRMcRXxs4KIazmb2KCQT7r9AQAIloG9/C68cw8x+qKLd6Zg6E9W1m9GyGExVT0aNIzVW8mM28EUO6z7bUA5jFznhZcmgdguII6CSGEELaSOJMQwslKXL7NclFBDuCAu0NCDwCXA7gLwMdE1NR3JRUTExQUuyoe3z9ttaEyhBDOoiLQFOxUvaq2FUIIIRyr3KtLU1ZeZObfkmsxEY6OnSrBoJd+R3xiEib8ujXwBhGq2FVmdxWEffRMTJANYBYzlzLzHgA74Q48VcLMU5k5gZkTWrUKvidSsascw15fGPR2QghnU5KjKRRkGl8R7QpLXJi39TDW7s1DfGIS0o+c9Lvegu2HQ1wzIYQ/3oGmolK5oBPCKc6fOA+HT7gn85u2bA/mbT2MXYf9n1PD3ZifN6PHc8n4IS0r8MoOsDunwO4qRIuKiQmIqDbcExP45oP7Ge7eTCCilnAPpctQXZHCkjLknPQ7uaYQIoypCDTpmqrX7LZmo+VChLvn/7cZj05Pw60frAAAXPXmYr/r/fnztFBWSzhYUWkZdkboxVM48M7RJD1/hHCG5elHqyx7dHoarn5rMeITk/DqnO0oLw//v9ilu44iPjEJX67ci9IyxtMzN9pdpYA+XLQbV76xCJ8t22N3VSL+mK1zYoIUALlEtBVAKoCnmTnXnhoLIcKNikCTnoh4dVIAXENEzYioGYBrtGVCCB8/rdcbvxXhbumuqhdCRvQeMwfXvLVYetPYxLtHU+r2yJys58TpUrurIERQ7v5kVY2vv79wN7o+m1zjOuHg3k+rvs99udYM4T1eWIK7pq6Eq8xczqfJs7cDAF6I4uGMocTMyczck5m7MfMkbdlYZp6lPWZmfoqZ+zDzOcw8w94aCyHCielAk56IOBENIKJsAKMAfEREW7Rt8wBMhDtYtQbABG2ZECH36dI9uPWD5Thw/LTdVRFRboHioESxBQlfI/1urwrenSIKSyIz2LdwhwxlF+Eht6AY8YlJdlfDVkNfS7Wk3PMmzMOKjFxMSt5mSfl2YJk2VAghTIlTUYiOqXrXwD0szt+20wBMU1EPIcyY+Jv7DtqQyQuwYvQVaNekns01EtGKyO4aCBWOnSqpeCyXLELYJ9oCTOv2HbNlv58ty8S4m/rasm8hhBDOEjbJwIUIpcEvL7C7CkI4WqTc7P3f+mx8v8aaJLm7qknYH0nYohDawfzTKIuAPDlC2OFP7y+3uwqmbd6fb+v+I+UcJ4QQdpFAkxDw30X6yIkiG2oiIl18YhK6jq757rrqDk0FxS7FJQKzNkRGzrD/+24DnvnRmiS55epHLDpCdTNeqpKRU4DBLy/AdW/7n/BACGFcqck8SsFwlZUjPjEJ/5yxPuhtb3x3KeITk5B9zJq8UkIIIawlgSYhAJwoqnohvm7fcRtqUtV9n67CzLXZdldDKBSoo4bqoXPrLRhGcTBfArGBlFeadi5ybo9/tXKfpeWv3ev+ve48LNOcC/NU5trZfugEpq/IBOC+aRCfmIRDYXYsDGUeyu7PzQYA/PzHAQBAVl4h8guDm0DgkldS8UdW6NtjkXPEFkIIe0igSQgAw15fWGXZ4l3OSHK7ZNdR/PuHDXZXQ1jsyxWZSsvL88oP9NO64Hsfbcg6jvjEJGyopoFvddzE3xTk4cY7oCgXLUIYd6KoFPGJSXjh1y3IPHoqqG2X71Y3G/vw/y7B2F+2VMr5dNHL8xGfmIQSCyZdMGJjds1BmYyc4D6/YJ2qpgdtfGISLn01Ff0nzMXxwhK/61Tn5inLVFRNhJmycsaEX7fisIwwECIsSaBJhDVXWTkSXpyHlRnmGpLeF+Ue36zaV3HH0i4yLX1kqenO+k/rzwSDSEGXppNFZ+4abz94Iujt79GmAB/10Qq/r1uVm+eRL9IQn5gUcArycLA319oLOruk7T0zOawVAcfFu8I/yCjU6fn8bJw7fi4Ad7Lpy19fiPjEJMzZfAh5p0qwft8x9Hx+No6c9H8xek+IjiU9n58dkv344xmi9ufP12DEezUHZdKPWNtTsO+4lIDrnDdhHuITk7Av17nD4iKoE2rYWr77KKYt24NBL82X9rAQYUgCTcJWzIxl6UcNd21/5seNOFpQgjunrqy0fGP2caV3Fz9enKGsrGDIiTV6rPcaqqli5Jz3n9QBA0M7PHmdqvs7sqoR/vu2w9YUbIPfNh6seFwSwrwoVssrCK43QrAkJ4vwVt0x6PGv1uKCifNwy/vLUeIqx8BJ81FYoj4fnUdWnnN/l54hagu2Hwm4rp51zDqYfxr5pwMPkRv6WqruMq2atEE416qMMzc1bnx3qY01EUIYIYEmYau35u3EPZ+sQpfRyfhq5d6gt/ceEtRn7BzkF5Zid04BRry3DONmbVZWz0nJ2wxtd6rYhUteWYC0zLzAK/vxf9/9YWg7EeZUZwO3QCTc7HVZHPxp07huxeOPFtkTrLaC93evOp+YEB5ZeYWYtzW4wHOfsYF70xh16av6gyKhlFtQHNT6VvVG9Tb45QXo/8JcpWW+NneH0vICCcXnFO1qOn2cLinDe6npFc+t7oknhFBPAk3CVu8sOHMSef5nc4GhwpIy9J8wF8/MdM8g9e1qfXe/th4IfliRXr/8cQDZx07jtg/9Dz+qyeb9+UjdUXOeqHIF039H0vAeIhpORDuIKJ2IEv28XoeIvtNeX0VE8dryeCI6TUR/aP8+tKJ+J07ru9tOCiJNljeRI6ANXmbx2Ijth6w7ttjJOxG8FR/hLkkCLuAO7Dw6Pc3uagRlm4FhymZk5RXiwhd/D2qbcB0SlnOyOKLaK6JmM9fJJDhChLs4uysghGqeGYv0uv6dJbrWO5RfhLZN6gZcLz4xCW0a18GqZ6/Cs//bFFRdPBZsP4w/fx64ga2ivVhYEhnD84goFsAUAFcDyAawhohmMfNWr9UeBnCMmbsT0Z0AXgFwh/babmY+z8o67j6q/gLak0Msc/INlZarnGkJcA+FyLV4yFSoWX3BlX0sdLM7RYoDx09XDNsUItxc9/aSKsdiq/zp/WWGZscN0zgTAOCy1xZWej76ut6YsSYLqf++XPm+wjUgFylUT5AihAg9CTQJoVOxS39A5vCJYry/MD3witX4YOFuw9sGK4IaUwMBpDNzBgAQ0QwAIwF4B5pGAhivPZ4J4D1SkXlbp1B+1qp3NfjlBT7lR84PxwqqA31OkHeqBAVF1gaBQpW4WQireIL/e16+XsnEDtUxEmSKNC/P3g4A2H/8NNo3rWdzbYRKO6VnqxBhT4bOCaHTkZOB8yB4z1D36hzj+QRO6ryYi8SLWRPaA/AeL5mtLfO7DjO7AOQDaKG91oWI1hPRIiK6tLqdENFjRJRGRGk5OTUPbawqdN+XnkSsZljx03t3/i71hdpk1oYDId/nkZNFSobTVueCifOCSt4bLGbGniCnrhfCqbqMTtY1a+3xwhJLh/BXEYHNhosnLwi8knCcCPwpCiG8SKBJ2MbfEDfVF0l5p9QN9UnedLDG1wMFfYKZsWb7oZO61turYBYc6ZkCADgIoBMznw/gKQDfEFFjfysy81RmTmDmhFatWgW1E9+fSLGrLKiecsH40/vLLSnXw4pfzRvzdlpQavWOF1oXjNsd4sSlWXmFGDhpPnqPnRPS/Rp159QVeP7nykOLixXOFCqE0+WdKsF/f9+J8ybMw/XvLMH0FZkVr+3OKUD6kQKUWRA4lnO+8AiU19JrvVuJiIkoIZT1E0KENxk6J2xz6wdVL4Tvm7YKXz9ykbJ9HD5RhOYNaisp67NlmRh3U99qXw+UV2TLgRPo2Ly+krp4HC80H0hT0TOFmS0dIqDTfgAdvZ530Jb5WyebiOIANAGQy+4oYTEAMPNaItoNoCcApZlovT/qTdn5uOk993S90x4Mv7ZbeQT0pvtiRabdVVBmRUYugOqngneSl5K3YWVGHlZm5OHFm88BM+OrVftwZe/WdldNiAqFJS7Ur22+mXy8sARN659phyzccQRnNa2Ha95aXGm9sb9swdhftlTZXnXOpwg4dIdEpPcY15nXEkTUCMD/AyDjmoUQQZEeTcJRlqXnIv2Ivt48eno/rdQuvkJhU3Z+ja8vTQ92mFVgKtpBKhIWb97viNm11gDoQURdiKg2gDsBzPJZZxaAB7THtwFYwMxMRK20RheIqCuAHgCUz0fv/X15gkwAdCV+t1P2sao95yKhDb7Oq1el6ouKUH8887cFNw28KkY+t6mLz/xpxScm4dOlezDm580YIsNfhAKq/pZV5SM76jWJwg9pWXjwszVVgkw1yciRXDXCEhV5LZm5BIAnr6WviXBPnFLk57WQOlnk7oXMzHjiq7XIlKHWQjiaBJqE41z15pkG2L9/2ICPF/u/3s/QMYPXC79uDbiOKncHSGKblhncbHh66M3lVJNVe8wH40rK7J+5Tsu59CSAFADbAHzPzFuIaAIRjdBW+xRACyJKh3uInKer+FAAG4noD7iThD/OzHkW1FHXevZ3Dqvsklesy8tTWlaO+MQkDHl5vmX7qM6qPWe+4pyCwDnYgrFg+xGl5QWSsuVMoClHRz45J3kxaZvdVRARxGVhnjJjztTn29X7gt76ijcWqayMDJwTHgHzWhLRBQA6MnONycbM5a7UL/90KUpc5egyOhmzNx/C5a8vtGxfQgjzJNAkHG3m2mxMSt7md4hYKINITjW1miBcMNQMnTNfhgrMnMzMPZm5GzNP0paNZeZZ2uMiZh7FzN2ZeaBnhjpm/pGZ+zLzecx8ATP/akn9rCg0zL2p5WU6kG/vzVKV+dwA91BZu+zLk7u8ThYoLwoRDSWidUTkIqLb7KijUMc76Fw7zlizW2X+Su8bHhsD9MSOZtF+viaiGABvAvhXoHWN5q40ck/to0Whm5VZCGGOBJpEWLjaTzfzJbuO2lAT4/Qm+A5Glp8hTWYVlgTfS8pxN5AdKlQBOSsSyFpFRZ4xFZwSLFWhoFh9D0OrktZHG6+8KNcB6APgLiLq47PaPgAPAvgmlHVbnn4U8YlJuO7tJYhPTEJ8YpTwuT4AACAASURBVBK+Xb0P6/cdw+spO5BbUBwWeWtOlzrrt3r4xJkehp2bNzBUxtaD6gLX3t/gCYtnJxWOFiivZSMA/QAsJKJMABcBmGVnQnBmYGeIJ9oQQhgngSZhSFk5o7TMeNLZIydq7r3wj2/XV3puZijIw5+vMbytaqp7TajIr+R74eDdKDZahvBP72w/eu/yeX/um7LzKy4Ob3x3aQ1bOYurzBm/nUj6CVtxzHtn/i6/yyPoYwuVgHlRmDmTmTcCCFlmd1dZecXw721eQY3RP23CLe8vx3up6bjwxd/RZXQy4hOTsGJ36PIfBis7z/x5USXviRPWZxkbQn/X1JWqqlOJ/P1WL5LOCdWoMa8lM+czc0tmjmfmeAArAYxgZtuSSn62LBO/bjhg1+6FEEGSQJMwpNuzyejx3GzD2+8/XnNDcNaGA3h0uppz2fztR/D4l2uVlGU2UHTBxHlK6qGSb+cXI71hwqgDjb0Uf07vzE+veOydXHybwrvfVtu03xlDN8xO+V1UWmbLBfj/m7Ee8YmV02eozFGTkVOADxbuxpRUGa6gSMC8KHqpzIvSPcjz+V0fr8TmAH+7qduPVEpknX+6FPGJSZUuFDNyCrAvV23PXLN/y6p5Byx2HjbWG+NkgFltgxEFARShg868lo5yML/qtcPNU5bZUBMhhB7m520VUc1VVo64WGvilfO2qptFac6WQ0rKWb/vGK48u42pMopdZagTF6ukPir4NsqN9E5yWsPeqfR+SnVr6ft9vPX7TuOVqUZZOaNA4UVNIFYMKdVjUlLlHG9mL756j5kDwH0B/vlDA8wVphMz45c/1N7dzcgpgKuc0bNNIwDqExEHy8pzTLhj5qkApgJAQkJCyA/C//5hA+b8c2iV5YNfno+DNeRc+/u36/H3b9ejcd04nNAmtNjz8vUgRbMgOC2Q4rQev67yMx3lHFY1R4mGdg0zJwNI9lk2tpp1Lw9FnYL1R9Zxu6sghKiGtN5EhcmztyM+MSlgbwjvpJR9x6XgRFHwY/xX71E+oVdIPPyF+V5Wi3ZYNyOHEb4NTSOdIaSxqo/ezyku1r5p5wa99Dv6vzA3JPvKtzE/yMdL9lR6rvI3/OBn6oauMXO1PT6sOI5e8caiiqnX9eRrs/pv//mfN1u7g9ALlBclbPgLEqfuOFJjkMnbCa9ZUx9RcG71yMx1VjJ8p/X43bz/TBsvGoIpQggh7CGBpih3sqi0IlD0oTaTw3VvL6lxm/umrap4XOwqx+ifNlU8Ly9n/FMbyjElNd3f5gCM5QGyYsruElfIUmBU2JenPoG3Gb7NTCND5yTQpI/TG/XMjKMFoUvOfdJAkNoqTv1uXkrehqGvpVY6znrcYVHeFsB9LO8zNkV5ucH27rBz9j6L1JgXxQ4uE/kWfT1kMMg632tmNrPSMo3lQbKKU48tgDPO3UUOS94unGv25ppHJzzx1VrEJyY5rhehENFKAk1Rau3eYxj6airOGT8X546fi2NB5B5all45B0nSxoMA3EGbrs8m42dtKMdrKTuqLcNIw8tsgCbzaNW7nIcDJCW3wqs1fC52qNqjKfjvJs8hM4c5nd6P1q42UqhnqztZFLoheoGYqYu/vBGqeHpefbt6H96YuwPr9rkvon3zMqn27P+qBrZUKAkyqGHkeORkevKiENEAIsoGMArAR0S0xco6Oa0HkFlOu8h0Wo8mb06o2h4/bTMncNjPSNTAc9PKE4jq9mxyTasLIUJESY4mIhoO4G0AsQA+YebJPq/XATAdwIUAcgHcwcyZRBQPd0PLc+W9kpkfV1GnaJF/uhT7cgvx47psHMw/jY/uq3nW0SMnijDwpflVlp/vk6R69qaDuO6cdrrrUd1Fz6wNBzCi/1lVlidvOqi77DPMnfUvf30hMiffYKoMwB1Qqx1nPEZrRy+qmvg2yo00rn5Zv9/v9ywqs6JXnkqhblfvPGxPfiZ/Oah+23gQF3dvaag8VRMXBPLugnS8uyAd7ZrUtXxfM9ZkBV4Jwd80kIu3wHlRmHkN3EPqQmJOgF4C4UZVYEfVT9VpgS+nibRgsgg9RuW/s3J2T97TvEFt+yolhDDfo4mIYgFMAXAdgD4A7iKiPj6rPQzgGDN3B/AWgFe8XtvNzOdp/yTIFKT+L8zFTe8txefLM5GyJXDybH9BJn+e+Hqd2aoBAP7x7Xq/y40MnSsuNR+g2etz59ZI+2bzAWfMkqVK2t7KwwyMNPqkmajPLw6fljcS2/unS8pQWOLCnVNXIEWbFKDfuKpDwsrKjR9fvHOehILeHDjBcuIFsQOrFHEycpzZo8QoVUPVVP32fl7v3OO+E/7mHVAFEebenLuzyvVNoFkxhRDWUzF0biCAdGbOYOYSADMAjPRZZySAL7THMwFcSaqmF4li/noRpSiaXc2JflibbbqMxTvNJ+L+0/vLTZfhJOlHKk+3XGag1Sd3JPVxQqO+JpEWRAWAs8fOQZ+xKViZkYe/fLm22u/AVWbsu/Gevj3c/bbRSE/T4LiC7G4ixxbrRdpnrOrtlCrKXXVachDVyKk/P4dWS/jx+fLMKj3GP1u2p5q1hRChoiLQ1B6Adx/7bG2Z33W0/AT5AFpor3UhovVEtIiILq1uJ0T0GBGlEVFaTo6zZu2yw9q9/pNd/uXLtcr28b3OoROhomLI2U/rK0/uY1eSzvnbAvc+s0u5JAO3nV0BqS9X7LVlv6HUZbT/3A2lBsfbXPHGIjPVcRTfHp9WcPrw0Wjk5BxCRqh6P3O3Ovc8rYoTzt2RFugUzpDqsBmehYhGdicDPwigEzOfD+ApAN8QUWN/KzLzVGZOYOaEVq1ahbSSVssvDH7mpVs/sL5XzTM/brR8H8FYv0/9TDJ2tW8ermEqZ7vzZRhppEtDUR+nzYbka0P2cburYBszQ+cihcLJx6oV7LGiY/P6FtVEeMxy+JDe4KkaOifntVBwavvBodUSQTp2qgTxiUkY8/Nmu6siRNRREWjaD6Cj1/MO2jK/6xBRHIAmAHKZuZiZcwGAmdcC2A2gp4I6hQVmxk3vLkX/CXMRn5gU1Mxv0eiAgrwk4dBwmLFmn+Ft7ZouPhw+VyfQO4QiWmadc9IFxsqMPLurYDsj38fe3OBmAw12F3ExMspeBEfVYcVBhyfL2NWr21uk9agTzjIpeRsA4MuVe+EKxd0UIUQFFYGmNQB6EFEXIqoN4E4As3zWmQXgAe3xbQAWMDMTUSstmTiIqCuAHgAyFNTJ0YpKyxCfmIQuo5OxyStZne/Mb9XJCxCQWpmR63e5XQdYJ90VrDLDmk31qImZj+vAcWuSBAfipICBMC7UgaYpqbtDur+aBDquRgMjf8dLdh21oCZnyLEleqiakVXVb8ZIvsJAasfaPZDAiZz6N+7Uegm9ikrLMNMrv+s9n6yysTZCRB/TZzwt59KTAFIAbAPwPTNvIaIJRDRCW+1TAC2IKB3uIXKJ2vKhADYS0R9wJwl/nJkj+rby6ZIy9B4zp9rX/U277euCAAGpO6eu9Lu83/iqMy2FwomiwO8pVHybDXYGwaoL/C1SkLA81GIkt79Sdv0qjeTnMsM3Eb2wV2gCjcHtQ89sqiIyqEq+req0bkWQ88qzWysv0wwnxHGd2qOp1OAEEcI5En1SgKzaE9GXmEI4TpyKQpg5GUCyz7KxXo+LAIzys92PAH5UUYdwccv7y2p8vd+4FGROvsGSfReVOqPL6CGLpubWwzcR7daDoZ2W3FteYQlaN6qrtExVd4SDdduFHWzZb6TacfikLfsNdkawSNKmcR27q2C7UHz/wV689e/Y1KKaCKdRFdhxWsDKyZzwHkN9g0OvmQpmOhY1s/oe5S65mSWEraQPrwLJmw4iPjEJT3xV84xvzIzth8xdQL47f5ep7e3gOyRFVSPQiIM+Qa5dh+07CRUWq5/yOHmz9dOT+1M7zhmHEiIaTkQ7iCidiBL9vF6HiL7TXl9FRPFer43Wlu8gomtDWW9fSSGYZt6fYHu0jP0lcpJrHj4hs6FNW2r9dNDVDe2uTptGEgCMFqriDT//oSa5eaiHEkcr+Zjto6PN9BQRbSWijUQ0n4g621FPo7YcqHozORxHDQgRrpxxdRjGysoZf/16HQBg9uZD+Gld9XdAVCSzfmPeTl3r2dWzxZ+tfg70TmFn+2Z9lvoZyKI5n4mW720KgOsA9AFwFxH18VntYQDHmLk7gLcAvKJt2wfu/HJ9AQwH8L4nf1w0CfbXM33FXkvqIewRih5NehPie0TDFPN2a9+0nt1VcHPY6cuK86nTRpk74SN3Uh7PaKKzzbQeQAIznwt3ipNXQ1tL9R6YttruKggRNSTQZFK3ZyuNGMRT32+odt1lFidN9bZT0dAbFV2aU3ccUVCTyGPFbOpR3l4bCCCdmTOYuQTADAAjfdYZCeAL7fFMAFcSEWnLZ2gzYe4BkK6VJwKIT0xCYYn1edh2ePUGterCZM/RU5Ze9PzlyzTbZoZ0ilJXdB+knKjvWY3trgIA590osSLuSnBWpMkJQZ68QmdOxHBFb2fl07JAwDYTM6cys2dq0ZVwzyweET5atBvxiUlYLXmbhLCMkhxN0SotM7iD0zM+SemslLr9CPq1b1LxvCjIu8geKpogM9dm4/VR/SueOyoZn42NLCtmtHFCo9FG7QFkeT3PBjCounWY2UVE+QBaaMtX+mzbXlXF/vr1WpwMMil+fGISAGBItxYoK2eUM6OsXPvHjGBHoHrKG9SlOWrHxSCGCLExhBgixMW4Hxudea3P2DMTDVzWsxUW7cxB99YN0b1VQ/c+YggxBMSS+7Hn/2Bc+9/Ffpe3b1oP3Vo3RCwBsdr78Lyv2CD3Mez1hZWed23VAC0b1EFMjDvhfQwRiDyPgz8+pmw5jJQtcyueEwEXdGpW5fv1PC420TPV8317XNqjpdd3fub9xMa431Own5Xvvjzlx2hlEXntR/s+Ygj4xcCwJs97ubRHSwDuw7ZnWnbPIc/fsjdu748Ozeobfl/RwilnjW2HTmBIt5Z2V6NCNJxPnfAOf9twEDeee5bd1aiiZcPadlfBanraTN4eBjDb0hqFyLP/24RvVu0DANz+0QrLcuMKEe0k0BSkgmIXzhmfgj/GXINXU3aEdN8ZOfrzCZ3XqXIC1exjpw3t82D+6UoNdRU5C/JOOScXip2NLCsasSqKjIbGtRlE9BiAxwCgU6dOurZhRtCBJo8SVzliYghxMTGoW4sqBYi2GUhmX86MU8WuSgGr8nKGS1EXu+OFJejYvB6OnChCDEELnKAieFJuMFBWnVMlLuSfLnWX6xWIc78nc7/lsnJGTIy7Z0NZeXnF+yhn99+J2R4YzEDdWmeCft6BuNgYQlFpmeFjt6+CYhfKte/CE8zyvJfycvPvpaDYVaksz2fk2Rez+R4rBcWuiv4gROT1WPsfBFQ8jvoeniFxqtiFBnXUNCWPFqjp2dK5hZrgYuO6tZSU440dEdrxYqI6RaVlqFvL/AjzaJ6EIlwQ0b0AEgBcVs3rQbeLAPuO0Z4gk8fonzbi5T+da09lhIhgEmgKAjOj3zj3nfv+E+YGWFu9K95YpHvdwhLfHkzGjuaH8osqBZpUdG2Xxr/bit25uGOA/hOyHnZ9tA4Z8rAfQEev5x20Zf7WySaiOABNAOTq3BYAwMxTAUwFgISEBF1v/IN7LwRQtZdJTfTcYbOzPG/bJw43dMERzP5eve1c3J7QESWuctSKdfeYCYSZ0WV0csD1PBY/PQydgrxIDeY93HBuO7wwoi9aNtSf4Nrod7LjxeGoExfcd/LWvJ1428CEE8HcDR4/aws+X55pWfkieGYGc50sUhdoUnWTQ9XgNDO9/KqTvOmQ8jLtknOyGB2bmw/qyc0t2+hq9xDRVQCeA3AZM/u9U2ykXeQk367Oqgg07Tx8Ej3bNLK5RkJEBsnRFITr3l5ieNtQn0g/WLjbZ//GyjlZXLkXhoq34aSbV3a2b1TNjOPNrvfz4aLdgVey3hoAPYioCxHVhju59yyfdWYBeEB7fBuABez+45wF4E5tVrouAHoAkIyROmROvkHJXe1Abk9wt4drx8XoCjIB0L2eR7BBpmBNufuCoIJMZgQbZAqVGKdlQxaO4ZAbFhVKbJwhN1Sc0MPKad97FAnYZiKi8wF8BGAEM0d0wtVZGw6gy+gkXPPWYny+zPoZWIWIBhJoCsL2Q8YTbH+8JEP3uiqGp/mWYbTEz5dlVnq+af9xgyWd4aRGhRMaWSrZ9X62H1STfN4MZnYBeBJACoBtAL5n5i1ENIGIRmirfQqgBRGlA3gKQKK27RYA3wPYCmAOgL8xs7HEZlEkCpKlRhUrenBU3YfluxBhSlXTINgAc3U+csYNFEs5oTnmgCpEJZ1tptcANATwAxH9QUS+N+8ixj++XV/x9zD+1624ePICeyskRASQoXM65RbozyuUc7IYrRpVvmv9UvJ23dtvyD6OCzo1q7Qs2GTem/bnV6mTEb4NgH15hX7XC6pMJ7RsIpRdH61TgofMnAwg2WfZWK/HRQBGVbPtJACTLK2gTgPjm9tdBV2mPTjA7ioIhUIRaIqTSJPjOKWTmarezqrezgmDefXCiRPO3E7q5R5tdLSZrrJy/0459viz//hpZOQUoGurhnZXRYiwFfUtviW7chCfmITnf95U43qvzNEfKFKVVNfb4p05prZfuMNYj9fD+UWVnv/fdxtM1QNQEww5GkTgryZL03OVlOMUdgXxpKGoVqvGoRleJc44u50zpni3UygCTdf3a2f5PkR4UnXDIuPoKSXlyE2x0JDPWTjVFW8sQnxiEt6ctxMFxZEfeBZCtagLNOWdKsHJolKcKCrFhF+34r5P3WlYvlq5DweOVz+7z/dp2br3sfOw/tnh/Jm/7XCVZZm55hpORgMBOw6rHxKlIihxyCcAZtSGLPNDAZ1EmmuR4c4BHQOvZAFVszXp9Y8re4R0fzU5u50k/wy256wRdWpFXbPD8Vxlxs8cxwrVzBQHALuPmGs7qRYN51MnxHicUAdhj837g5851w7vzN+FfuNS8Pbvu5BfWGp3dYQIG1E3dO6CifOqfW3I5AVKZrfxvSsX7N0af9NZf+0zFWewgp3lx0o7FQSv7G6YHD5RhDaN69pbCT/s/lyEGnYlTK5fO7SnhO6tndMlnZQNuAlfpQaSHweb3FySgTvP/O3Gc/wu2pmjrDfgz3/sx+jrz1ZSloeZm1LRcD4104ssK69QyaxzKvKSivC0JN3caI1Qe+v3nXjr950AgDE39sGfL45XlhNOiEgUVbcWXSGaQSTVp9H2mU9C7UD8nff35prLjWT3ibzYdeZOedKmg6bLszsnkJELslCw+3MRatjVbLm6T5uQ7s9JzTNpKwLtmtQLepvbEzoEtX6ww/MGxDcLvJKwjcq2RYlL/XlV1TB7p1HVq/uHtfp76/vyzQVq1IoMZ6YwOHwiMn87Qo2Jv21Fl9HJiE9Mwh8RNjpCCFWiKtCk50Dw5cq9pvezy2fo3ITftga1vW+wwIrGV6jt99NLywhPgMfugIoFabiUkDCTMOOaUAeaHBTcCUF6IsfrYaCHWbDfYYuGtYMsP/K+GCIaTkQ7iCidiBL9vF6HiL7TXl9FRPGhr6U+Ww+qG/ri3TNKVQDL7ptsVikoVjN852ANKSMCCUVOt1Dzvom4yGRuVBE9bp6yDPGJSej5/Gws3XUU5RF63BEiWFEVaLrtwxUB1xnz82bT+zF7d+a3jZV7/JwOQd4Mq20/pCbXkyfoZvchvMyhPYdUVMuZ70yEQr/2TUK6v1AMV+veuiE6NAvcU8doXWY+Phh1bco7tOjpy5WWN6hri6C3CfZziw0ycBRpl7JEFAtgCoDrAPQBcBcR9fFZ7WEAx5i5O4C3ALwS2lrql7TRfA9lD++2k6oE0RlHnZX3yQzvz6TEZf+Z2mxPeyc6dkpNzrFIuEEcEvb/jJUqcZXj3k9Xoeuz7p5Ow15fiN+3Vs27K0S0iKpAU7iKhJtGZpOZe3jOSXbPUuLcu6ROrZcQVTWuZ31OqN+fugyp/7484HrXndPWUPkJ8c2xfeJ1hrY14ofHB+PeizrhhnPboXOLBiHbb3WC7XAUbJA+Ao9oAwGkM3MGM5cAmAFgpM86IwF8oT2eCeBKUtS163hhCY4XlqCg2IViVxk+XpxhuszcgmLsOnwS8YlJpsphBrYeOIHcgmJ0f262qbJyThYjLTPP9Ey5e46ewtq9x0y/t52HTyIrz1xgZuSUZThdUoYtB/Jx/TtLTJW1dm8ejpwowpGTxoeHfblyL44WFGPsL+Zv0GYfK8SPJobxAUCP55KRW1CMq95cZLiMgS/Nx5zNB3GyyFyPsQsmzkNuQXHFP+/0EcJLBFzf1GTP0VN4ZHoa4hOTEJ+YhCmp6ZJMXESVqEsGbsSRk2rGwhu1bl/4j/39bFkm/np5d9Pl1I51x0btjvPYPXSvOk4d0ifcXry5H55X0GsyUgzp1jIk+6kVG4OvHxmEez5ZhdaN6vi9uGpcr1ZI6mLGZw8NwID45hgQ37xi2fXntEXypkO21SnY64RGdYJsdjjzUGtGewBZXs+zAQyqbh1mdhFRPoAWAI6a3fmj09OwJvOY2WIqufDF35WVZTaA4jFgkpo6DXt9oZJyrnlrsekyNmbn4+yxcxTUBrj1g8A9/PVIUPTdX/JKqukySstYyW/x8a/WmS6joNhVqS7THkzAFb1DOzQ9HETbJByvpezAayk7Kp7/86oeeOjiLmgSBu0PIYyQHk06rMrIs3X/hcUuW/evomt8jom7Zt5OaZ+F3eOfNytKgqkaR+BVWSQZFWTiZDt886jvNa91Qtlbc1CX5rhrYCf89Nch1dTFmQ3eF2/uBwD4+P4EDOvVusrrU+6+QOn+EjoHmXw72KFwNaz/54u7VFnm1KC+ExDRY0SURkRpOTn68sk8fEkXjLupD569vjeevraXknqMu6kPru2r5iL6/sGd8cKIvqbLeerqnrjq7Kp/L8GadEs/jLrQ/HH7T+e3xyOXVP19B+s/w3vjrCbmZ7y9tm8b/OvqnqbLeU7RLIFPXN5NSTkTRpr/7Vx/TlsM72ush6tvXTz/erRuZLq8SBTtbdb//r4L/V+YW9HjafqKTLurJIRS0qNJhymp6bbu3+58QAfz1STyViFt7zFc3acNFu+yN0lj5lE1QwFVk7wAzhYOd++GdGuJ7x67CHdMXWn5vkKZ6DkuNgYv/+kcAEDm5BuqDIUxW5OJI/tizC9bAADD+7bFnC1qehndM6gTEuKboXdb/1PIExHev+cC5J4qUZJjcOYTQ4IaJqTyG3zuhrMxbdkehSU60n4AHb2ed9CW+Vsnm4jiADQBUCX5IzNPBTAVABISEnQ1FIb3a1fp+d+GdTc9LOyhi7vgIS1IaLasCSPdgdVbL+yAfuNSDJfzjyt7AAAWbD+MP3+eZricewZ1xj2DOuO1Uf1Nvbc37zgPAPDJUuO/78zJNwBwB2XyC0vRf8Jcw2V9dF8CAGDZ7qNYafBmavMGtfHo0K54dGhX09/7f4b3xn+G9zZVzqbx16BR3Vq4f3C84XL+GHs1mtZ3T1hgpi5pz1+Flg3rGN4+akR3nKmKsb9swVitHQEAnz04AMN6mw+YC2GXqAk07c4xnhBSVSJro+zOB2T3/r15Aim7j9gb6GnV2PwdRSv8/McB02WY+VsRNVMdVzm3QxNszFbfu85IUmijHr6kCz41cfGlitnv5vJerQFswUu3nIM7B3RE12eTFdWLqg0yeVx/jjt44Ak03TWwk5J962F1rNA5Zx9l1gDoQURd4A4o3Qngbp91ZgF4AMAKALcBWMB2JyasRpeW6vKEeSftrxOnpsN9g9qR2cxV1ROkbq1Yw9teq6DXj0r1TLwXjxhF3WwbBjtEWAg/Hvp8TaXnjerG4bvHBqN764aoregYKYSVouZIeOUbxpMD2s3o0IHSsnLUijV/IHI5KNDkmXrW7uEU54Z4dq6aFJa4UF9hY/qHNHMJOUX1VF+Tj7upL279YLniUkOrT7uagyihYnboXMfm9bF94nDUiYsJaU8tb/cP7ozpK/ZizI3mhrP8+uQluOm9pZWWjbmxDyb+trXKuip66Q2Ib4ZRF3b0W5JD4yuGaTmXngSQAiAWwDRm3kJEEwCkMfMsAJ8C+JKI0gHkwR2McqRLuqvLs+bdXlHRdgHMBQ5UBtFUU3UDsIGJgIiqYKAqcQp+M0HnkKtGbCTM4iMc52SRy28euz9f3AW3D+iALi0boE6c+YCrEKo46ywhKnjnINqQZazHgqpgzJzN9iWa9fXbRvM9dlTwBLycICNHbe8uJ/VgizS6gxk6V2vRoLbxygThYQW5Raoz8ryzLCu7Jg8OiVdeZt1asbYFmQD3sKPMyTeYDjyf06EJ/jO8d8Xz0df1tvQ38MPjQ3D7gI5+gwKReDRi5mRm7snM3Zh5krZsrBZkAjMXMfMoZu7OzAOZ2fzUcBZReT1txZ9Ox2b1DW+rooeMVerVVlM3f3nR9FJ1k+DJYeYnilHF+/hd20TgKtahOf9EZJq2bA+G/3cJej0/pyLfk+ffpa8uwNq9eaZnUhTCiKjo0RSOF84nikorxomnHzE2lEnVjeBNDkp8fbF297TY5lxEHy7ajU+8Zn6yU06BmkTrHk4KokUaVd3yPeJ97ri3alRHWeJ9b2Nu7GPZ8DYVd6GNGD+iLz5fnlnx3KnJwO1y5dmt8cqc7QCAv1xWfaJesx/bRV0rH0dXjr4SF70831yhImT6KezdW9eCO/GN6xlv5taKde4xQdXxykyvpL7t1QSa6tZy6D1vEx+x6nO9VYhoOIC34e5d+QkzT/Z5vQ6A6QAuhDtP3B3MnKlq/1YM/ReVZeWd1jXL5Ij+Z2FY71a4uk9bGfoplFHySzJzoCKi0QAeBlAG4B/MbDz7YzU2Zh8Pav2ycq7oEOhbPAAAIABJREFU9mo26XNWXqGh7VZm5GF4P/f496XpxmY0XrUnD5f1bGVoW6eK076XRTvtTQa+5cCJSs/tHN6xdNdRv7NRGSWBpvCyYdw16P+COynsfRd1xpvzdlqyn4FdmmP1Hmtm4Ez551C8OW8HUrYctqR8PVTHmZySe8qoHq0bAgA+uu/CimW3nN8e/1tfOW+1kY/tkUu6VCRFnvHY4EqvtWpUOYFuhI2cizi+wW4z7hvcWVlZHmYCMnYFwfVwQlw80oeHRfjbAxHFApgC4GoA2QDWENEsZvYeI/0wgGPM3J2I7gTwCoA7VNVhRUaVOQ6ETWZtOIBZGw4A2GCqHCKgVcM66NqqAXq3bYxurRqgZcM6aFS3Fnq1bVTREz9cgrHCHNOBJjMHKiLqA3fugb4AzgLwOxH1ZOYys/UCjM8Y0e3ZZAzr1Qr3D4nHQ5+tCbyBHy/P3oYnh3XHpa+mGtr+8a/W4uP7E/D0TON/8A9MW42l/xmGxB83GS7DY8/RU1hp8oSweX8+jprsfTPmly2me6gNfTUV854aanhIIgAczC/CyCnL8NXDAzFtaSbS9hq7AI9PTELqvy9HYYkLa/bkIftY8DP8fbp0D9o3rYfbB3QMvHI1nvhqLf7v6p645q3FhsuIT0zCBZ2a4ru/DFaWX0ME1qReLXzz6CAUFpch1sI78N//xR0QMDu7kD+92jbCa6P6I2WL8VmUzFLdoync7wgSUcUsVx5v3XFe1UCTgY/t2evPrnb2Ld+2p8SZrEdkPKCn8q/GaUPVhvZw7o06J/TAdEIdrBQOs8SaNBBAumdoLhHNADASgPf120gA47XHMwG8R0SkYnKCaWF8I0ZUjxk4crIYR04WG57R0k6N6sThZLHLVBn9OzbFhix9nVxiCPC9rL2sZyvsyyvEHj+dXc7t0AQtGtRGDBGICDHkPhbHxLjbbUUlZShnRu24GJSz+xztOcefLi3DI5d2DWknFBUtYcMHKm35DGYuBrBHS3w5EO6ZVmyVuiMHqTuM95r5aFEGPlpkLq3Co9ONT8nrcckrxgJdvoa9vtB0GTe+uzTwSjqM/7VqQtpg7MsrRK/n55iux4as4zhnvPkLYxWf7YTftmKCn0S9es3efAizFeTiWrfvuLIk9EK/Id3UJeS1S+O6tUK+zz7tGmPrQXfvRNXXTAMcMrTWiWJiCNMeTEC7JvWqvEZEmPXkxRjx3jL3AunSZLkYIpQZ/JxV5iSz4g63mWDIZb0iP9Bk5s9LVR2KSp3ZizoKOly0B5Dl9TwbwKDq1tEmMsgH0AJApaEWRPQYgMcAoFMnfbOe5p5SP8xfCLPMBpkA6A4yAVWDTABwMP809h/33/HgYH6Rth2jvNz9P7P2nBnlDLjKy1GvVixiiCpejyFCnVoxOF5YYug9GaUi0GTmQNUewEqfbdv720mwB7HCEvM/FCFE8FTOgKcXETUH8B2AeACZAG5n5mN+1nsAwPPa0xeZ+Qtt+UIA7QB4juzXMPMRK+p6Re/WWLDdkqIBAONv6mM6GBst7hjQEeNmbQEAtPYZsmXWJT3CP/jnzz+u6I53FqSbLueK3m2qfe3cDk0rHodhisWoovJi3IoL+0gNFqh6X9nHjKV3ANQNndtnMMWE1eyc2CHcMPNUAFMBICEhQddR++lre2NK6m5L6yXs16hOHLq1boizmtZFUWk5GtaJQ/fWDVErNgZtm9RB03q1ERdLaFy3FhrXq4U2jeug1MWIiyVTs2IKZwibbzDYg1i9WrFI6NwMQ7q3xDvzdwW1r1EXdsC9F3VGxtECbMzOx2fLMoOu749PDEb3Vo3wv/XZhi/6liVegQ1Zx/HXr9cZ2h4A/hh7NRZsP4Knvjc35nb7xOHIP12KQS8ZT9KqogxPOb3HGO+R9OkDCRjWqzWOnCw2lXR20i39cNuFHXDitAuxMYQLJs4LuozXR/XH0J4t0aJBHczfdhidWtTH8P9Wnbo0EM8QF6PDmmY9eTHO7dAUWw7k44Z3jPU8e/W2czGkWwtD2yqQCGA+M08mokTt+X+8V9CCUeMAJMA9ImetNszXE5C6h5nNdyMM4J27zke/ccpT0VV48OIuSgJN/7q6J96Yt9NRMwKp5j19uWfyBavcd5H6/DN2+OuwyoEmqy/GWAbPWc7MN6i0R5MFv6VIDRaoel+ZucaDPKqCXU79ipxaL4X2A/DOudBBW+ZvnWwiigPQBO5cuyKCXd6rFW67sAMu6NQM7ZrUDf1xNDQTKosQUBFoMnOg0rOtIUSEmU8MAQA8cmkXnBvEEKdXbj0XMTGE/h2b4rp+7QwFmi7s7B42YfSib/HTw9C+aT20b1p1aIFeb4zqj6b1a+NPF3QwHWiqWysWdU3mT1BRxqU9Wpou48qz3XfS2zapa6qcewa5LxxbNTJen9su7FDx+Jq+bQ2V8XgNM0Lp5elB0Pcs4zMI3Z5gPEeUAiMBXK49/gLAQvgEmgBcC2AeM+cBABHNAzAcwLehqaJbuOTu+fuVPTDivLPQuYW6ZL9O45t42krtmpo73jiF2eNvsFo1DN13FK3MXEN4EruqYEWgKVJ7NDlBC0V/m079iiI9BxWANQB6EFEXuK+97gRwt886swA8AHdKk9sALFCRn0mE3i3nt8ffhnVDl5YNIz6Rv3AOFVc8hg9URDQLwDdE9CbcycB7AFitoE6VBJv7wztPQKgb1R6dWtSveDy4awtDMzNc2lPN0I0LOzdTUo4KHZq5A2+X9miJJbuMzcanQndtRiYnGGxfLyInacPMB7XHhwD4G5fjb5iv91Ddz4ioDMCPcA+rs6wx9fS1vfBayg6riscTl3fDBwvNd0mP5CATAHRrFbq/40hNLGv1tVgzhYEM4Z/7t2nscNexef3AK+lkxbWPmTvxZiceCQdm/n5V3TRxaq8zh1ZLGS2VyZMAUuCeNXwaM28hogkA0ph5FoBPAXyp5dDNg/saTzjQny5oj79f0QNtGtexJYWFEP6Y/iWaOVBp630Pd+JwF4C/qZpxLpI0b2isod2ojprkuk66o/zAkHgAQJzN0fgbz21n6/69Nbd4yI9TENHvAPx1+3rO+4kWxA72CuEeZt5PRI3gDjTdB2B6NfUIOumlr1EXdqgx0GQ2KHH3wE5KAk3CnBvOaYekTe74Z6TeQLQqgPbtoxfhro9X4plre1tSvjjDKRfU53VsGnglHbq2VBMgX7Qzx1E32iLVsN6t7a6CX1HQownMnAwg2WfZWK/HRQBGWbX/ri0bIMPPzFqien8Z2hX/uqYXasfJpDvC+ZSEPM0cqJh5EoBJKuoRqWINnuzq1VbTG+uei4xdTFsh82gherdtjNgYew+wTjrA92yrtlfGtX3bIGXLYaVlqsDMV1X3GhEdJqJ2zHyQiNoB8Jdtez/ODK8D3EN1F2pl79f+P0lE38A9+6XfQJORpJe+Wjeui/gW9U3lx6iJyl4Gwrh7LupUEWiKgmsWpQZ3a1GRe05YyykX1I3rqbk5dlP/s5SUIyOEQuMsk2kMrBKpNwdE+Jjzz0vRq00jx/b6EyIQ6VsXBuweS2s00GUFz7SMtWLtrVPtWOcEmmopDrr1btvYkYGmADzDcydr///iZ50UAC8RkecW9TUARmt545oy81EiqgXgRgC/W13hhU8PM5y8XYSHOK+/zR5tGtlYE+s46PQgDJILav+i4eLOCe/QqR9zx+b1cbQgtFOBi+j19LW98NfLu0XFcUdEDwk0hQG77zY66aDnCbpd2LkZZm8+ZFs9ru5T/dTcoeadU6xhnTgUFLtMleegrzsYkwF8T0QPA9gL4HYAIKIEAI8z8yPMnEdEE+HOKwcAE7RlDQCkaEGmWLiDTB+H/i2ISOP9t3RpdzU585wmPA8XwpuTzvEqqHo70RCAi3PETTNnftCPXdoVT5iY9VmIQKY9mIArejvnekII1STQFAaG9myJH9dl27b/vu0b27ZvX56Z4szMjqaCXUniA/nXNT3xgsmp7cMxaTEz5wK40s/yNACPeD2fBmCazzqnAFxodR1FYE9d3RNvztsZsv1ZfSHpXbwzLujUi7AYRVSKtK9Q1TnM7pt8oVDb5t7hgHOPIZEWgHWiq/u2wUeLMuyuRshc27cN3r3rAkel3xDCShJoCgN2B1XqKwiqDO3ZSkFN3MO6APsbJk5tfkhKCRHOQj1M2Orgz9ntnBOkt8qwXs5M5Cv0s/t8qpqq93OFQ5NUK+WAL9/+Ggi7DOvVOuIDTZf2aIl37zofTaNk4h4hvEVNoOnK3q0xf7u//MCBtWxYB0cLihXXSL+D+adt2zeg5uLv4m4tFNTE/nxVwr8bHDQLXzhZnngFhkxeYHc1HMMB1zxKNVA0/beTNaqrJoGzsI9Tem447fyuqudySwfN3OvLCZ94NPQcE/5F8jf/y98uRn9FM2kKEa4ivxWsefuu89FvXIqhbec/dRn6T5iruEb69bI5iayKRmjPtmreg9Maok5zurTMdBmGvm7pSWXIWU3r2V0FR3HSxANCH/nKwp9TvsNainoYqno7qpobdw9yzsy9TuSU358IPacEuVXZPnG4Y1NrCGGHqAk0NTRxZ7lJfXvv2NaPgLviqu5YNdGmP46sU5M6nln5zHCVlSuoiRDBkzvbQoRepP3VqUsGHmmfTFVOeIvOzQspd9BEYNf1a4t37zo/YvMwCmFG+EcwFEu8rrfdVajCTJDMrJvPO0tJOfEt6ispxyPS7oKooiJH09FTMp2v3aL15924npySwsGA+GZYk3nM7moIReR86p+yQJPDkicO7NK84rETgjzSUz16heuhJzaG8Npt5+L6c9pJDyYhaiCteh/D+7a1uwqO8ubt5ykpR3WOgnA9OVnNWc1ZIYIzLBqS70aAuBi5cxtJIu06v3WjukrKidR2Rm2vnhcXdLY/h0yPNg3troIQugzu2gJfPzIIMZF20BTCItJa9BHfsoHpMibd0q/S8+dvODuo7SeO7Gu6DqqoamjVr30m4v/Y0K6my1N1iO/Wytj3bfdwyuqouHFq5LNdtDPH/I6FUkMUJeAPJVUXiMJa3ucFh3XWEIZE1kXT5b3UzHIbDReTdvaY94j8T1lUJxy++8Z14/Dx/QnInHwDvn3soqg4LgihStQGmtKev8qysu8eWDnx44ND4oPa/r7BVdd/7+7z8cII/QEop80C5t01X0U3U1UBsEu6tzS0XZ048+/Bk29KpXKbrvoKil227DcS3HCONX+r/do3qXhcO079ob57a7kLHa28hxTZOSOrUCNSe+6Ypep60mmxWO/v2wnDJqMhF5YIT/+/vTsPs6I6Ez/+fXujWbqBhu6mZWt2aJC1ZRHZdxpFEzEao2hAzKhZNDFBcWFcW42TxGd+M4lxEtGMCWpMMCZBgWzGZDRo3JWA2BEJKkGNTnRMlPP7o86lq2/f/dZyl/fzPPfpunWr7jlVt/pU1alz3rP3uuU8vXEJi5rqw86KUnmpqCqa3BU+fXt04fIVTR0+/96aaXHXvfHk8SmnE33i9iJA3IrxR7E6jQqrWo+6qvlxEeLNN3r0LQV2gRNWRZPK3Nc+MZGLl4zy/HuN61ho6Ol9S6H52s2taLmLzQ8+zH6kSxWuQntA79VZsE93b7v8q9hy9TKskC+nRKRGRLaJyG77t3eMZSaKyO9F5DkReVpEPuF9Prz+Ru+0tbZo6yWlslRUFU1XHt/EhQtH8uLVSwFYc9wQHv7yPADOnDGY40bEb91ySvNAHr10QSD59MKJk/qHnQVf5fLJKVXGh6sYL76ygK+tclJFWQkfm+zv/6sf/y5+HL9hKS8tgAJF5bxUbu7scltF5G0ReSCIfJ0zK/vu7BHHNMbcpEDtf/t9T77Hj5agn2ge6Pl3ZiMXSr5Ce+CXJ9YDO4wxI4Ad9n2094AzjTFjgaXA10XE46BeufPbb1g+hj3XLuP3l8zn95fMDzs7ShWEoqpoEhE+v3BEh65bA2u60dbawlUrxyVY01FfXcn3z5kOwMbjm+hekXr3qbbWFnZdszRpBcnKJKO8PfuvS1JKb2RUcMXR/apSWi9f9OleEXYWsuZHbATjQTVR7pz2i0dVpffdKN0X7+d4EBctaF7EckvFMxsX8+QViwNJq1DoKDsZS+XmDuAm4IygMuXlueiylqbkC/lsRI516z3a1Y051+pUci0/KjArgU12ehNwYvQCxpg/GWN22+m/AG8A3gRAy0HnzB5KWWkJDT270tCza9jZUaogFFVFkxdmDOtDW2sLZ80cwtYvzE5r3S5lpbx8fQvbL5oT8/O91y3nG6dOSvgdPbqU0dbawl1r43fzg46jikTynSu8uLDx6iQQZmyAk6cM8Pw7C6iRicrSpIHtDx5PnzY4xJxk5tLl6Q2ikKmqynK650BA3HySbtxBdUTSmzsAY8wO4N2gMuXlabDpqOqM1nvMwxbjXX2qCI20gE/XKNeDvlxovaODLiig3hhzwE6/BiQMQiQiU4EK4CW/MxaGH513bNhZUKogaUVTFgbWdMtoveF1PWhrbaGttYX7L5gJQFNDdVp9gY8d3pfnErRuio4LtWRsv07LLBvXeV4y0S2lMiEetJnx6lotzGu+SYO872Jw2ItR5zLYJ00Nmd1cKIcfh2GkK3BVpT+VKH5Vao7N8EY1V/3PJQt8HXwiDF186FJUJNK6uUtGRNaJyE4R2XnwYG6M/FmeRkzKR9bP54nLF9HW2kJdtXeVH9HXP1612Mr0ms8t02uOynLv/udOcLWc9+J6TOUmEdkuIs/GeK10L2ecfvBxz+gi0gDcCZxtjDkcZ5mcK4sS2XPtsiPTVx7f5Mv1uFIK9DFulvZcu4zhG36e8frjB/SirbUlo3VjPYU/YcJR3LSqc+Dy/r3aWwC502tc/9O00iwt6Xyxc8tpk2hqqGbhv/06re/KBZlcYr18/fKs01058Sjm+RJMOZwmTWUa4yYr0TcfXuzNqspyfnPxPOqq8yug7Y/Pn8lHXtSY5oh+PgRiD1shbpNXRGQ7EOspzgb3G2OMEZGsDnRjzK3ArQDNzc05809zzYnjuOzHzyZdzn1d4qcvLR7Jxp88H0hasbhHqZ02pIa7Hn0l7e/4z09N6TSvrqoLb7yb/qiPerYuDsaYuE84ROR1EWkwxhywFUlvxFmuGvgpsMEY8z8J0sqoLArrYW9ZaQlPXbmYfW++12GEXqWUt/SxZJbKSktoa21h52ULqele0aGWPAiRkZ+6V5RywoSjuOW0SR0uaiJqq2LfbN61dhq3n30M04fWdPrs306Z0GneSZPan4TtuXYZ2y6czQkTjkprqHMvTixenZuG1qbfQsuLpu/JukhmyotWJs2DOx8LyejwxNnx66nyoD7d8i6eTnlpSd7ludgEVUGQj4wxC40x42K8tgCv25s6Et3cBc2Lljpu7sENTp4ygD9sWEivbh3j0CVqkR2RakzKZD413buuw+fPG5b2Ou6K2RF1mcXLnNrY+bz82IbMWkoO6dv9yLSeuuPLmZpbf9wPrLbTq4Et0QuISAXwI+AOY8y9fmQijMNv9QynPOjZtVwrmZTymVY0eaRvjy48cfmiTk22/RYZFv1H58/kltPiV15Ulpdy9sxG7lwztcP8Y4f3Ze6oOn6wbkandT42uXMMoXpX8/ay0hJG1Ce/aDp+QuIA55nwKs7BlMGF1Vz2sAc1TZm0VijVIWCLToFfhCvlh6Q3d2EY0y+z7qrfPfuYmPO7VZQdCQ/w1VUTqK3qwkMXdoxpmUpcNK+6vHl5XXbxktFZrd+QYWvAePsrkxbxNa7BVPTMXbRagUUishtYaN8jIs0icptd5hRgNnCWiDxpXxPDyW5mfvWluZ3mfW7BiOAzolSR0q5zeW5MQ3XKFxpXHj825e/t2TX2KFixWkslc/XKjulmemHjHmo4rIujC+YNDynl1HjRoimT7yjVx6JZ8WMYbaX8kgsBjfNUK3C3iKwB/oxzI4eINAOfMcaste8fBkYDPUTkVWCNMeZBvzLVO4NRXM+dM5R5o1Lv/l1XVUlbawt/fOWttCqQ2lpb0u7i77eBNV3Z9+b7Ga2byb5O12UtY7jmpy/E/VxjrCljzCGgUwR+Y8xOYK2d/h7wPT/z4fe5pNHVei+iT4/8CiegVD7Ts42K6akrYw/3PW+0M7Lp0TGam0YCm0eL7gaT6XlllWuUtrCCgUe2PxsLfInN5PAitI3JoK1KjNBdgRKRGhHZJiK77d+YTdVEZKuIvC0iD0TNHyIij4rIHhHZbJuMB6a0RPj3T/rTnVIpP+lIl6kzxhwyxiwwxoywXezetPN3RiqZ7PtZxphaY0xXY8wAPyuZMrV+aWYteyYN6p1SS+hc9osvzs1q/Y/HaC2ejcddgw28ePVS1hw3JOHyHVp4aZ2xKiLfP2d62FlQqqhoRZM64qkrYlcuuXUpK6WttYWffPa4Tp+NH9ArxhqdK5r69cwsvoe7UijTpyAnT8nuAm/sUdn3575sRVPW3xFPJpVEXogVPyJg64EdxpgRwA77PpabgDNizL8B+JoxZjjwFrDGl1wmoKP/KKXyweOXLQy0VdtL16U+AMfjAYzwWF5awt3ndg43EMvcUZ0fTt18ygRO8DCkQJ8eXdjxxTn89ivzqCwvTeu30fOOClMQR9/tri6+seLRKqX8k1VFUxqtCFbbZXaLyGrX/F+JyC5X31//mnqopHp2i91dzmuTB3WukPrs/ORd0gbXdG4Cm66vruoY4DzdeAkVCWI93HXONKqTDCX/2KULOgTi9FqVFzEtMqirOjpOJWOAVgKb7PQm4MRYCxljdgDvuueJc1U+H4gEu4y7vlJKFbO21pbAu56UlkiHB2FzRjqVN6umDGDXNUu577xjuf+CmTx+2cLA8jZ1SA1VSc73ALefPTXm/FiDrcSz9Quzki4zrLYHA3q3B3X3YnRcpfwWRH313FF1vHj10sAryJVS2bdoStqKQERqgCuBacBU4MqoCqnTjTET7SsnRmBR3kk1ftQXF4+id5KKrlhPBrNVlmafr5IEQa+PHdaX313Sqct7B3XV/g4LfsLE7J+SZtImKgdigdcbYw7Y6deA+jTW7QO8bYz50L5/FeifYHmllKXX7SoIPbuVHwkwvunTU2lrbeGmVRPoUlbK5EG9GT+gV8JKpnRaRaXqmY1LElYC/friuXE/Kyst4dMzE3dxixidQbD2eDfUN358fNRyaX910dBuwYWjsrxUYzMpFYJsK5pSaUWwBNhmjHnTGPMWsA1YmmW6yic7L1vIC1f5+/PEuwBaNzvxsMF+tLjqWuHtMOpejZKTqUyCtUfL5OJqWG2PrNNNRkS2i8izMV4r3csZYww+DoomIutEZKeI7Dx48KCH39s+/ZEXwbZ8Ul7qZHREnf+/ucp9XpQ5Knx/umZZ0mVuXpV6K5xc49fIqKP7VXeqxLr3MzNoa21hcJ/ErZevOL4p6X6/9zOpddGLJfpB38vXL+eUYwZ2mFco9UyV5RoJJB/52XXzlzFGnFNKBSvbu+JUWhH0B/a53ke3FviuiHwE/BC4xt4kqpD0DaDGP95pZfnR/bhh64tx16uryq410FUrUx91zw850OrHN/U+t9QCMMbEDb4hIq+LSIMx5oCINADptI48BPQSkTLbqmkAsD9BPm4FbgVobm72rLxyHx6PvPRXjh3e16uv9tSC0fVsfe61uCNTquIy9qj0W1uo3JNs5Mudly0M5PrAT7uuWcqoy7Z6/r2lJZJy6+1oFWUlHdade9MvaTqqmjOmNzKivkfW+/zl65fzf/887PlDtVyz/OiGsLOgQlRVWcY/PjzMBx8ePjKvsU+3BGsopYKQ9BGAz60ITjfGHA3Msq9YQXoj+fClFYHy1iPr5x+ZTndklbIE8Y+8cOaMRl+/P2K+HVVu4/Edg35vTjF4aDaaGsK56cuBi9j7gUj8t9XAllRXtGXXL4GTM1nfKx2C3RfMc2ZV6DTmReFwB8p97NIFtLW2sPe65bx8/fK8r2SC/Gh996uL5/Efp09hxrA+nuxzEUl4fs61/99MHwieMX2wxzlRQfDq8LvmxHGdjp1cO7aVKkZJ7+ztELzjYry2AK/b1gMkaEWwH3C31T3SWsAYE/n7LnAXTgynePm41RjTbIxprq31PlaP8kb/Xu0jyt0cJ9hlvCbs6Z4SHvjscez44hyeuHxRmmt2lOmTyHhuO7OZF69eyllR8ReOCWBktkQxpFIV1sh1WWoFFonIbmChfY+INIvIbZGFRORh4B5ggYi8KiJL7EdfAS4SkT04MZv+K9DcA2NclYTHDusTdPIpG1nvdJmrrcr/G0+lVLsfrJvBMxsXs+uapUfiCZaUSEHdsP34/JlhZyGn5Nove+aMRtpaW/jvtdNSXufONVOZNCjmWESqwKyeEbtCccKAXqwY791Ijkopb2TbhCSVVgQPAotFpLcNAr4YeFBEykSkL4CIlAMrgGezzI/KAb+5eF7CIYYH9O4ac355mi2axvXvybDaHtR0r+CKFU0xl/nWGVPS+s5E4p3gopWUCJXlzhPETZ+OW3eas/Kx86ox5pAxZoExZoStHH/Tzt9pjFnrWm6WMabWGNPVGDPAGPOgnb/XGDPVGDPcGLPKGPNB0NvgjufRJYfjTXxuwQjuPncGzQFUnKrcNVxjdBWkqsryvGj5k6mJA0MfIVWlYGaKXcd/8cU5zBrhz8PnPH3oVtDiXXc09u1O9y5ltLW2sH7ZaK1QVipHZHs3k7QVgb3huxr4g31dZed1walwehp4EqeV07ezzI/KAYP6dEs4ukO8p6N9e1QAMKRvd36VZhC/s2c2xpy/ZGw/LmsZw51rsq/wSRbDIpb66vxr9aGXViqRstISpg7RSqZid/8FM/nDhvgPFJTKVam0Yv7hv/jf1T0XeNVYbVBNavFwPjV9EA9dONubRIGhAQxEovLLZ+YM0wplpXJEVsHAjTGHgE5Ces5DAAAbAklEQVTjuRtjdgLuVgTfAb4TtczfAe+am6i8tbjJiSEvIjxx+SKqKssoLy1hz7XLGL7h5wBcuHBkwu8QEcY0VPPCgXc6fbZ21lDvM52iPt2diqZ0KqmOHdaH3710yK8sdTIty0qDVIdoVunItQ4NSnXUraKMbhXhjrKpVKbaWlt44pW32PLH/Qyv68HBdz9gVfNABvTuWlBdBZPxKh7gQxfOZvTlyQOtLxvXwMj6Kh67dAFTr9uRcNnd1y5jhL0GdLt4ySjOnDGY7lr+FKUzZwzmjt//+cj7sEd7VkrFp/+dKqfUdK84Mu0ODv75hSOSrvuTC2Zy9MaHuPakcVx091Oe5210v/QDbddWdeHnn5/FkL6Jhzl269fT/xHc3KoqOxYDNd0q4iwZ26wRuTk6Wj4rovscpZQKxeRBvZmssX08EQkXkEykS1xddSWfnDaIuSPjd3uLF07hvLnDklYGbv3CLPa9+T7n3LEzpXypcKR7rTOstgcrxjd0qGhyD2KglMotuRsIRBUNd+VStP88fTJ3nZNaUMiy0hJeuHrpkWFu1x7nbUubkfVVGa03pqE65YswgEVj6jNKJ1PRMZlSbQIfMSfBhaJSSimlcpSHDzXSDXlw3UlHs3hsv7TW+fnnZ6XU4mx0v2oWNdXz9U9MTOv7i4WI1IjINhHZbf/GrXEVkWo7eMq/e56PNA/ApqOqO/3+F8xP/iBaKRUOrWhSoVs3O37XtmVHN3DssPRazFSWl/Li1Uu5dPmYbLPWQVCtTAb0Tr2ip3e38qzTi47J1LNbOd/81OSk69148nge/MJsT0a6Ux2VaJMmpZRSPvPyVDO4T3oPqVJxxvT2QViuO+noDqOzpmLlRB2JLI71wA5jzAhgh30fz9XAbwLJVQbS6TGglAqWVjSpUJ07e6gvwRwry0vTqgCpjDHK18lTBnR4P/ao9LvOZSKddPwKxjt/dPJWVac0D2RUv8xaeanEtJpJKaWU3yo9HGHQj9hWV584jhtPHg/Axyb3T3t9EeGFq5bys8/NSnvdfByBNw0rgU12ehNwYqyFRGQKUA885EcmEh0ydVXJB9N59NIF9Oya/QNXpZQ/tKJJheoSj1sdZWreqLpO88ZFVfgEFSC0pER4+frlKS1bFieGQbYqykq4ZNnoTvPPmaWBv5VSSqlCkOpAJQ9/eR7Lj27v5vatM2KP5ZNocJATM2xddErzQNpaW9IKQeDWtaKUpoAeFOaRemPMATv9Gk5lUgciUgLcDHwpyIyl4srjm4DUKqOUUuHRYOAqFJvXTadXmkGn/XTLaZM6jW4yvC681joiQltrC43rf5r2ur++eC5zbvpVyssvGNO5kg3g3DnDqKos59IfPQM4I8CUl5bwuQUj+OhwYT/qC5v2nHNMHNiLJ/e9HXY2lFKqaD115WJ6di3nP05PPlD0hpYxfOeRl2N+1vrx8V5nTSUgItuBWEGwNrjfGGOMiMS6qDsP+Jkx5tVkD1pFZB2wDmDQoEFp5DH+Zw29uvLGux/E/OzsmUM4W0c8VirnaYsmFYppQ/vkVLerWKObjKzvwbQh7aNZmDxpRz24T3r91T85NbWLgsg+qqosz6lKwkLk1ZDT+c6LGGRKKaXSd+6coey6ZmlaXZNKS4QvLR4Z87NMWyR5ZcHo2A/VCpUxZqExZlyM1xbgdRFpALB/34jxFTOAC0SkDfgqcKaItMZJ61ZjTLMxprm2NvUBYhJd65w/d1jK36OUyk1a0aSUNdVVqTR1SA111ZVsPncGj21YwHfPOiawrnNBS7Rd/Xpqs2QVnmXjGsLOglJKFZ3Tpg7kkmVj6JJBDKd/mTvchxxl75LlncMBJFKgl3wR9wOr7fRqYEv0AsaY040xg4wxjTjd5+4wxiQKGu6puurKoJJSSvlEK5qUsn5wznQuXjKKFeMbuPvcGUfm11VVMq/InoRFzB5Ry6opA/j1xXPDzkpRKfAL3JTNHpn6k1GllFLeOH9e5pVFpSXCS9elFmcySOmGQygr7BF1W4FFIrIbWGjfIyLNInJbUJlI2HWup1Y0KZXvNEaTUlZJiWR1ceWHPt0rOPT3f4SWfllpCTetmhBa+qq49dMLTaWUCtTQ2u4M6N0tq+8oLRHmjqrlV7sOAnBpmq2JwnD6tI5hBEoK+ImPMeYQsCDG/J3A2hjzbwdu9z1jLoW795UqHlrRpFQO23bRHCZfvS3sbCillFKqCGz9/GxPvuf2s6d68j1BiY5FVVaqVR1+0z2sVGHTrnNK5bCa7hp0uxjV6pC9ShUcEakRkW0istv+7R1jmYki8nsReU5EnhaRT4SRV1U8rlo59sh0W2sLFWXFeWvQo7Ljs/c8Gf+loN1+9jFhZ0EplYXiPJsolUeG1/WIOf/smY3BZkQFpl6DYCpViNYDO4wxI4Ad9n2094AzjTFjgaXA10WkV4B5VEXmzBmNtLW20NbaEnZWQvWp6YM7vP/Hh4dDyknx6NYlfseavj26MHdUe3zUl6/PvbhfSqnEtKJJqRx319ppnDNrCF9Z2h7j4OZVEzq8V6pQ3fDxo9m8bnrY2VDKCyuBTXZ6E3Bi9ALGmD8ZY3bb6b/gDDuuUfGV8kBbawv/tbo5Zqvh6sqOXec0RqD/+vfq2qFFnVuJDcb+uQUjOG3qwIId+VmpQqYxmpTKcXXVlWxoaQJgydh6htbGbuGkVCH6xDGDki+kVH6oN8YcsNOvAfWJFhaRqUAF8JLfGVOqWCwYU8/DX+7L6Mu3Ak5X9c/O7zwQTN8emXVhXzauX1b5KzZnzmjkii3Pxf38okUjA8yNUspLWtGkVB7RSqbYRKQG2Aw0Am3AKcaYt2IstxWYDvzWGLPCNf92YA7wNzvrLGPMk/7mWilVaERkOxDrTnOD+40xxohI3CgwItIA3AmsNsbE7MMjIuuAdQCDBmmFrFKpqiwvZe91yxEhbkuZAb27ZvTdVZV6a5WuT04bxF2PvhJ2NpRSHtOuc0qpQpBK7BOAm4Az4nx2sTFmon1pJZNSKm3GmIXGmHExXluA120FUqQi6Y1Y3yEi1cBPgQ3GmP9JkNatxphmY0xzba32rlMqHSUlkrA7VqZdtc6f17l1lEpsfP+eYWdBKeUDrWhSqgBtWD4m7CwELWnsEwBjzA7g3aAypZRSLvcDq+30amBL9AIiUgH8CLjDGHNvgHlTSnlgQO9uYWdBKaVyglY0KVWAziq+EenSin0Sx7V2OPGviUjc4Awisk5EdorIzoMHD2aUWaVUUWoFFonIbmChfY+INIvIbXaZU4DZwFki8qR9TQwnu0qpdJWWaNDqdGmcb6UKk1Y0KVWAyktLOHnKAAC2XzSbu8+dEXKOsici20Xk2Rivle7ljDEGiBv7JI5LgNHAMUAN8JV4C2p3FaVUJowxh4wxC4wxI2wXuzft/J3GmLV2+nvGmHJXN17tyquUUkqpvKMVTUoVqK+umkBbawvD66qo7pr/wSm9iH2S4LsPGMcHwHeBqd5vgVJKKaXyxbUnjYs5/6xjG4PNiFJK5SGtaFJKFYKksU8ScVVSCU58p2c9zZ1SSiml8sonp3YczfHLS0ex/aLZbDxhbMzlbzx5fBDZKjgnTOjPoqZMIh4opXKZVjQpVQQaemY2TG8eSSX2CSLyMHAPsEBEXhWRJfaj/xaRZ4BngL7ANYHmXimllFI5RUR4+frldK8o5RunTuS8ucMZXlcFwOUrmjotv8qGLFDp6VpRyrfPbA47G0opj2XVn0ZEaoDNQCPQBpxijHkrxnJbgenAb40xK1zzhwA/APoAjwNnGGP+kU2elFKd9exaHnN+XVXcmNd5xRhzCFgQY/5OYK3r/aw468/3L3dKKaWUykciwnNXLe00f81xQ7j6geePvG9rbQkyW1lJ4/5tEHAbMBAn9uVyY0yb3/k7d/ZQv5NQSgUg2xZN64EdxpgRwA77PpabgDNizL8B+JoxZjjwFrAmy/wopVKwcuJRAGz/4pyQc6KUUkoppQKU6v3bHcBNxpgxOLEr04p/mak1s4YEkYxSymfZVjStBDbZ6U04sU06McbsAN51z7OxUOYD9yZbXymVvYVjnP7vL1+/nG+cOom21haqK2O3dFLhmDyoV9hZUEoppVQadl62MOwspCvp/ZuINAFlxphtAMaY/zXGvOdnpnp0cTra1FVV+pmMUiog2Q5FVW+MOWCnXwPSieTWB3jbGPOhff8q0D/ewiKyDlgHMGjQoHiLKaXiuPWMKXx42ODU8apcdM9njuWwMWFnQymllFJJvHTdcg4bQ3lp3oW8TeX+bSTwtojcBwwBtgPrjTEfRS/o1T3ak1csynhdpVTuSVrRJCLbgX4xPtrgfmOMMSLi2x2SMeZW4FaA5uZmvRNTKk0lJUJFiVYy5bLSEqEU/Y2UUkqpXJfL52wP7t/KgFnAJOAVnJhOZwH/Fb2gV/doZflXYaeUSiBpRZMxJm57UBF5XUQajDEH7PDg6fTdPQT0EpEy26ppALA/jfWVUkoppZRSSrl4cP/2KvCkMWavXefHOAM7dapoUkqpWLKtOr4fWG2nVwNbUl3RGGOAXwInZ7K+UkoppZRSSqm0pHL/9gecBgG19v184PkYyymlVEzZVjS1AotEZDew0L5HRJpF5LbIQiLyMHAPsEBEXhWRJfajrwAXicgenJhNWkuulFJKKaWUUv5Iev9mYzF9CdghIs8AAnw7pPwqpfJQVsHAjTGHgAUx5u8E1rrez4qz/l6c4TKVUkoppZRSSvkojfu3bcD4ALOmlCogGnVNKaWUUkoppZRSSnlCTB4OpS0iB4E/p7h4X+CvPmYnV9PW9PW3z/X0BxtjapMsk9O0LNL0czztYk8/1bS1LApW2OnnQh6KPf1cyEPY6cfLQ16XR3lWFmUiH/MM+ZlvzXMw4uU567IoLyua0iEiO40xzcWWtqavv30xp5+L9HgszvSLedvDTj/sbc9VYe+XsNPPhTwUe/q5kIew08+VPIQpH7c/H/MM+ZlvzXMw/Myzdp1TSimllFJKKaWUUp7QiiallFJKKaWUUkop5YliqGi6tUjT1vT1ty/m9HORHo/FmX4xb3vY6Ye97bkq7P0SdvoQfh6KPX0IPw9hpw+5kYcw5eP252OeIT/zrXkOhm95LvgYTUoppZRSSimllFIqGMXQokkppZRSSimllFJKBcEYU5AvYCmwC9gDrE9z3YHAL4HngeeAz9v5G4H9wJP2tdy1ziU2rV3AkmT5AIYAj9r5m4EKO7+Lff9P4D2bh532sxpgG7Db/u1t5wtwi/2up4HJrnRW2+V3A6td86cAz9h1bqG9dVsN8AjwAfCuXeYd4As+b//twBt2f2+283cCDwewvfuBD4H/daVxE/Cm3Q9/A2bb+Y3A+6598M0U00j0u71j09/j+q4f2Xkf4AwT6+ex9g7O8bbLtc7TNu3/A14DnvRx+1P+HfPxFe930bJIy6IY21vMZdFem857QKOdvxn4i037n8BLWg6FUxYlKY+CKg/caTwNPJBiGbbHft7o0bF7H/BD4EXgBWBGwPvgIE4Z8TzwfaDS730AfAdn+Ov3XGnUB7jNfwP+ATzvSuNxO+994BdAL5/Kpsj+e8Pug2djpHErYIC+ARz7SfdzPrzi/RYBpv8d+5s+65rn+/9xlnkOrQzOIs+VwGPAUzbP/5rG/1xW5bYH+7sU+CMBnms8yHOb/f2eJMBr9rj58fofNxde9sB4CRgKVNiDuymN9RsiOxuoAv4ENOHc3HwpxvJNNo0u9iB8yeYhbj6Au4FT7fQ3gX+x0+fZ923AOcBmVzo30n7SXw/cYKeXAz+3B8x04FHXgbXX/u1tpyMH12N2WbHrLouTxo04FQ2Dfd7+m4HJODcV37Tz7weeCmB7v2nTfs2VxgZgq13+TuAvdn4jrpNS1H5IdZ9Gb8dsnH/o91zb8RZweYzt8ONYm23z9bc4abwNXOnj9qf8O+bbK9HvomWRlkUxtreYy6Itdtv3YY+1qDS+jVPhVurTthdsOeRFWZSkPAq6PHgA50YrcvGfsAyz06fGOa4yOXZ3AXfa6QqgV4D74DrgZeAy4Aabr7P83gc4ZcNDwD5XGg8F9bvb9G8BDrrSeAwos+v9xZW+5+dBO70ReBB4NiqNY3Eq4P5Me0WTn+fChPs5H16JfosA8zAb55zjrmjy/f84yzyHVgZnkWcBetjpcpyKmOkp/s9lVW57sL8vAu4iwHONB3luw5ZDuXBch17Y+PHCebr0oOv9JcAlWXzfFmAR8W9uOnw/zoloRrx82B/nr0BZdH5d67bhPC36K+1PNXYBDXa6AdsCBfgWcJornV3289OAb7nmf8vOawBedM0/slyMNPYBj9j3vm4/zs3Du8AMV14O2eV93V6b9oux0gBOwnnSH1mu0w1Omvs0VhqNOE/tI9uxM7Kvo5bz/Fiz08NwWhOIOw37/n1glSufnm9/Kr9j2OVKhmWHlkVaFqW1vRRxWRTZJrucRK27D6dV2ZHlvN72VH7DsMuULMoOT8si+x2R8iiw8gAYYI+DfTgVTqme48qij6tMjl2gJ06lxoNR+yKofTDJbvsY+/4BYElA++BN2lvzzAD+HtTvbqePAT6Ik8Z+4Iexju10ft8U9t+bOGXUkTSAe4HfAQdor2jy81yYcD+HXdaEVR5lmI9GOlY0+f5/7HH+AyuDPcpvN+AJYFoa/3MZlVke5HUAsAOYT0DnGo/2cRudK5pCOz4KNUZTf5wTccSrdl7aRKQR58T+qJ11gYg8LSLfEZHeSdKLN78P8LYx5sMY+YusY3BqCquAC+1n9caYA3b6NZybv0zS72+no+fHSqMOp2l2hN/bX+5apx6nNU2fgLb3wzhpfNpOR9YZIiJ/FJFfi8gs1/Kp7tN42/FPV37fwe5roBkYnuG2p3KsAXxkX32i5s/CaaJvXN/t1/Yn25Z8pGWRlkWZbG8xl0XgdJNxl0WzgNdxnt76ve2FWA6Bx9sTVR4FWR58Haf7bOR/PqXjyn4efVylmi93GkNwuttMtcffbSLSPcB98Efgq8AfgBF2mx4PaB+8EzW/MuDzwEGcm7hYaVTgdBXJJP10jqF3cVoj9Af2ichKnEquF+x8otfzeB+ksp/zQa7mPYj/Y0+EUAZnk9dSEXkSp+zchtOax+8yK1tfB74MHLbvgzjXeMEAD4nI4yKyzs4L7fgo1IomT4hID5x++F8wxrwD/CdOy4+JOE8ubvYx+eOMMZNtOp8WkdnuD41TlWhirumdcvu6x74Pcvs7CGh7iU5DRDbg3PS9ZmcdAAYZYyZhm1SKSHXKX576djxK+77+X+D4VNPw2Gk4TSYjgtp+5aJlkZZFRV4WnUbHSkYth0IUozw6ws99KyIrgDeMMY/7lUYKyoBxwJv2+Ps7TleEI3zeB72BlTgVXn8DuuPE+ghVmP9TtmyMPBAJUgVwKXBFUAlq2RWcXN7XYZXBmTLGfGSMmYjTSmgqMDrkLCUUda7JN5Fr9mXA+SFdsx9RqBVN+3ECpkUMsPNSJiLlOP/E/22MuQ/AGPO6/Wc5jBMvYmqS9OLNPwT0EpGyqPlHvssYs99+3gOnWe5U4HURabD5a8CpGc4k/f12Ono+7jSATwLvG2NeD3D7/+la53Wc2AeHAtresqg0TgdW2L8DgP3GmA+MMYfs/ngcp1Z+ZKppJNmOcld+a1z7+lWc5oqZbHvSY81OR/oMH4rMt+t8DKfr3H67zX5uf7JtyUdaFmlZlMn2FnNZBE73pEhZNBinHNoc0LYXYjkEHm1PrPKIgMoDYDFwgojsA7ridGn4BikcV/Zz93GV6bH7Kk7Lmj12mXtx4rwEtQ9OxonRFCkj7gNmBrQPqqPm/1/A54FanMr2I2mIyFk4ZePbsbY5jW1Ldf9V4bT+3g+Mx6nwewrnHFUDPCEi/fzaBynu53yQq3kP4v84KyGWwVkzxryNE8x8Bv6XWdmYiXOuaQN+QHDnmqwZY/bbv2/gDOYS1DV73AwV3AvnBLwX5wQQCbI1No31BbgD+HrU/AbX9IXAD+z0WDoG+9qLc9MeNx84T+bdAcXOs9PnA7fhnMxOxSlMfofzxOomooLj2ukWOgbzeszOr8G5IOltXy/j3DRA52Bey+18dxpPAT8Lavtx+kq7A/D+BHg6iO21ab/mSuNynKf3tVFp1AKldnoo7Tdiqe7TeNtxEh0D8L7i2o5DwH0+HWuRff1Z2oOBR9I4Hqc1w17XNvu1/Sn9jvn2SvS7aFmkZVGs7aWIyyK77fuAu11p7AV+E5WGlkMBl0VJyqOgy4P1OC3cIgFaUznHnRp1XGVz7B4ANtrpjTZvQe2D/4czctPlOAFeN+Gcv4PYBz+lYzDwbQH/7jfQHgy8Baf74PM456THXMejX9dJp+LEank2ThpttMdo8vPYT7if8+GV6LcIOB+NdIzR5Pv/cZb5Da0MziLPtdgRIXEeEDyMUznse5nl0TEylwDPNVnmtTtQ5ZoO7Jo9bp6C/qcO6oUTSf1POE86N6S57nE4zcqexjV8Ns6IP8/Y+ffT8WZng01rF64I7PHygXNx/BjOU7F7gC52fiXwM5xgrO/ZdTfYz/rgBCbbDWx3/eiCc/Hxks1fsyudT9s09gBnu+Y345wsXwL+nfYgv5E09uAMGzvYtY6f278Z5+Ltn3a7X8cJGPdIANv7Gs5TssM4Nyxr7Prv2t/hfdqDTH4c5yLvSZu/49PYp/G241273R/iPC1dg1M4fGBff/R4X7uPtXts+v+weYikvwEnHsNrUWn4sf0p/475+Ir3u2hZpGVRjO0t5rLoFde2HwDW2M+ewGlBciQNn7a9oMuhbMuiJOVRkOVBJI3jab/4T3aO22M/H+rRsfsQTlykp4Ef41yIB7kPDuF02XsBpyzs4vc+wKnYewPn//NDnEqehgC3OVIGuq9T3rZ5ed9uwzd9Kpsi+++Q3QeRPNznToOOFU1+HvtJ93M+vOL9FgGm/33az/WRY8r3/+Ms8xxaGZxFnsfjXDs8bb/3ijT+57Iqtz06TuYS4Lkmy7wOxam0egrnGimwa/Z4r0ghppRSSimllFJKKaVUVgo1RpNSSimllFJKKaWUCphWNCmllFJKKaWUUkopT2hFk1JKKaWUUkoppZTyhFY0KaWUUkoppZRSSilPaEWTUkoppZRSSimllPKEVjQppZRSSimllFJKKU9oRZNSSimllFJKKaWU8oRWNCmllFJKKaWUUkopT/x/MBVSfsCzNasAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Raw-data preprocess analysis parameters\n",
    "fs = 44100\n",
    "\n",
    "num_instruments = len(inst_files.keys())\n",
    "print(\"Sample waveform plots\")\n",
    "plt.figure(1, figsize=(5 * num_instruments, 3))\n",
    "file_ind_inlist = 0 # 0: let's take the first file in the list for sample plots\n",
    "for i,instrument in enumerate(inst_files.keys()):\n",
    "    sample_file = inst_files[instrument][file_ind_inlist]\n",
    "    x = ess.MonoLoader(filename = sample_file, sampleRate = fs)()\n",
    "    \n",
    "    plt.subplot(1,num_instruments,(i+1))\n",
    "    plt.plot(x)\n",
    "    plt.title(instrument)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y9-kzgr1Z3B3"
   },
   "source": [
    "The recordings include a series of isolated notes for flute, violin and Eb-Clarinet and isolated notes for vibraphone. We could split the series of isolated notes to create the samples.\n",
    "\n",
    "Splitting/segmentation could be performed based on energy threshold. Below, a list of heuristically selected values are used. \n",
    "\n",
    "### Preprocessing of raw data: Segmentation, splitting, alignment,... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0oWIfJH4Z3B5"
   },
   "source": [
    "While common preprocessing steps (such as amplitude normalisation) exist, there is often some dataset and task specific preprocessing tasks required for grouping, cleaning, and format change. \n",
    "\n",
    "In the downloaded dataset, recordings include a sequence of isolated notes played in sequence which could be split into isolated notes. We would like to investigate the option of splitting using a fixed energy threshold (which would help us seperate notes using the silence regions). Let's define a function to perform this operation and visualize some samples to observe the effectiveness of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nhjFnVCLZ3B8"
   },
   "outputs": [],
   "source": [
    "# Raw-data preprocess analysis parameters\n",
    "windowSize = 4096 * 4\n",
    "hopSize = 4096 * 2\n",
    "NRG_threshold_ratio = 0.01 #threshold expressed as ratio with respect to the maximum value\n",
    "#Let's put in a container to be able to use as a single argument in function calls\n",
    "params = {\"fs\":fs, \"windowSize\":windowSize, \"hopSize\":hopSize, \"NRG_threshold_ratio\": NRG_threshold_ratio}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oxgM16oZZ3CE"
   },
   "outputs": [],
   "source": [
    "#Function definition\n",
    "def split_file(filename, params):\n",
    "    '''Function to define split boundaries based on a fixed energy threshold\n",
    "    '''\n",
    "    x = ess.MonoLoader(filename = filename, sampleRate = fs)()\n",
    "    NRG = [];\n",
    "    #Main windowing and feature extraction loop\n",
    "    for frame in ess.FrameGenerator(x, frameSize = windowSize, hopSize = hopSize, startFromZero = True):\n",
    "        NRG.append(ess.Energy()(frame))\n",
    "    NRG = np.array(NRG)\n",
    "    NRG = NRG / np.max(NRG)\n",
    "    \n",
    "    #Applying energy threshold to decide wave split boundaries\n",
    "    split_decision_func = np.zeros_like(NRG)\n",
    "    split_decision_func[NRG > NRG_threshold_ratio] = 1\n",
    "    #Setting segment boundaries\n",
    "    #Inserting a zero at the beginning since we will decide the transitions using a diff function\n",
    "    split_decision_func = np.insert(split_decision_func, 0, 0)\n",
    "    diff_split_decision = np.diff(split_decision_func)\n",
    "    #Start indexes: transition from 0 to 1\n",
    "    start_indexes = np.nonzero(diff_split_decision > 0)[0] * hopSize\n",
    "    #Stop indexes: transition from 1 to 0\n",
    "    stop_indexes = np.nonzero(diff_split_decision < 0)[0] * hopSize\n",
    "    return (x, NRG, split_decision_func, start_indexes, stop_indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UOCdtjxHZ3CL"
   },
   "source": [
    "Let's visualize three files per instrument to check if the splits look fine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 684
    },
    "colab_type": "code",
    "id": "ajXtZE7pZ3CM",
    "outputId": "e47b8d5f-51b5-46c5-c68e-6896cfa78177"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample plots for waveform versus energy and splits based on energy threshold\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_instruments = len(inst_files.keys())\n",
    "print(\"Sample plots for waveform versus energy and splits based on energy threshold\")\n",
    "\n",
    "file_indexes = [0, 1, 2]\n",
    "for file_ind_inlist in file_indexes:\n",
    "    plt.figure(file_ind_inlist, figsize=(5 * num_instruments, 3))\n",
    "    for i, instrument in enumerate(inst_files.keys()):\n",
    "        sample_file = inst_files[instrument][file_ind_inlist]\n",
    "        (x, NRG, split_decision_func, start_indexes, stop_indexes) = split_file(sample_file, params)\n",
    "        #Plotting functions for checking the split decisions\n",
    "        plt.subplot(1,num_instruments,(i+1))\n",
    "        plt.title(instrument)\n",
    "        plt.plot(x, label = 'sound waveform')\n",
    "        plt.plot(np.arange(NRG.size) * hopSize, NRG, 'g', label = 'NRG')\n",
    "        plt.plot(np.arange(split_decision_func.size) * hopSize, split_decision_func,'r', label = 'split function')\n",
    "        plt.vlines(start_indexes, ymin = -0.5, ymax = 0, colors='b', linestyles='solid', label='Segment start')\n",
    "        plt.vlines(stop_indexes, ymin = -0.5, ymax = 0, colors='k', linestyles='dashed', label='Segment stop')\n",
    "\n",
    "plt.legend(loc=\"best\");\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WYukVMTrZ3CV"
   },
   "source": [
    "The decision boundaries look fine, let's use the function to \n",
    "* get segment boundaries and crop segments, \n",
    "* amplitude normalize them and save all segments in a new folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "6CE1UdkXZ3CY",
    "outputId": "f81bcf71-66a3-4ae7-e07e-333393e0525f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "193 segment files created\n"
     ]
    }
   ],
   "source": [
    "segments_dir = os.path.join(main_data_dir,'segments')\n",
    "if not os.path.exists(segments_dir):#creating the directory\n",
    "    os.mkdir(segments_dir)\n",
    "\n",
    "segment_files = []\n",
    "for instrument, files in inst_files.items():\n",
    "    file_count = 0\n",
    "    for sample_file in files:\n",
    "        x = ess.MonoLoader(filename = sample_file, sampleRate = fs)()\n",
    "        (x, NRG, split_decision_func, start_indexes, stop_indexes) = split_file(sample_file, params)\n",
    "        #Croping segments\n",
    "        for start, stop in zip(start_indexes, stop_indexes):\n",
    "            if stop - start > fs/3:#let's only keep segments larger than 1/3 second\n",
    "                x_seg = x[start: stop]\n",
    "                #Final check for amplitude (to avoid silent segments selection due to noise in split function)\n",
    "                if(np.max(np.abs(x_seg)) > 0.05):\n",
    "                    #Amplitude normalisation\n",
    "                    x_seg = x_seg / np.max(np.abs(x_seg))\n",
    "                    filename = os.path.join(segments_dir, instrument + '_' + str(file_count) + '.wav')\n",
    "                    ess.MonoWriter(filename = filename, format = 'wav', sampleRate = fs)(x_seg)\n",
    "                    file_count +=1\n",
    "                    segment_files.append(filename)\n",
    "\n",
    "print(len(segment_files),'segment files created')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ecE-GitzZ3Cb"
   },
   "outputs": [],
   "source": [
    "#If required, you can use this cell to delete all files in a given folder\n",
    "def delete_files_in_dir(dir_name):\n",
    "    '''Deleting all files in a directory\n",
    "    '''\n",
    "    for root, dirs, files in os.walk(dir_name):\n",
    "        for file in files:\n",
    "            file_name = os.path.join(root,file)\n",
    "            os.remove(file_name);print(file_name, 'removed');\n",
    "\n",
    "#delete_files_in_dir(segments_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pmTShfNLZ3Cg"
   },
   "source": [
    "Segments saved into separate files. Check the \"data/instrument/segments/\" folder.\n",
    "\n",
    "## Feature extraction \n",
    "\n",
    "Let's compute a list of common features for each of the files and form a data frame including features and categories. We will be using the [MusicExtractor function of Essentia](https://essentia.upf.edu/documentation/reference/std_MusicExtractor.html) that would compute a large number of features commonly used in MIR literature. \n",
    "\n",
    "Let's first run it for a file and check its output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Mq0W_HPVZ3Ch"
   },
   "outputs": [],
   "source": [
    "import essentia.standard as es\n",
    "\n",
    "#Running music extractor for a file:\n",
    "file = segment_files[0]#simply pick the first file in the list\n",
    "features, features_frames = es.MusicExtractor(lowlevelSilentFrames='drop',\n",
    "                                                  lowlevelFrameSize = 2048,\n",
    "                                                  lowlevelHopSize = 1024,\n",
    "                                                  lowlevelStats = ['mean', 'stdev'])(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SbPZ_i7JZ3Cn"
   },
   "source": [
    "Essentia-MusicExtractor extracts a large number of features. For simplicity, let's only keep low-level descriptors which are represented with a single scalar value as our feature set and discard other features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "TvOzjojcZ3Co",
    "outputId": "3cc6950d-ee2c-4a4e-9bff-44b18d1e769a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subset of features to be considered:\n",
      " ['lowlevel.average_loudness', 'lowlevel.barkbands_crest.mean', 'lowlevel.barkbands_crest.stdev', 'lowlevel.barkbands_flatness_db.mean', 'lowlevel.barkbands_flatness_db.stdev', 'lowlevel.barkbands_kurtosis.mean', 'lowlevel.barkbands_kurtosis.stdev', 'lowlevel.barkbands_skewness.mean', 'lowlevel.barkbands_skewness.stdev', 'lowlevel.barkbands_spread.mean', 'lowlevel.barkbands_spread.stdev', 'lowlevel.dissonance.mean', 'lowlevel.dissonance.stdev', 'lowlevel.dynamic_complexity', 'lowlevel.erbbands_crest.mean', 'lowlevel.erbbands_crest.stdev', 'lowlevel.erbbands_flatness_db.mean', 'lowlevel.erbbands_flatness_db.stdev', 'lowlevel.erbbands_kurtosis.mean', 'lowlevel.erbbands_kurtosis.stdev', 'lowlevel.erbbands_skewness.mean', 'lowlevel.erbbands_skewness.stdev', 'lowlevel.erbbands_spread.mean', 'lowlevel.erbbands_spread.stdev', 'lowlevel.hfc.mean', 'lowlevel.hfc.stdev', 'lowlevel.loudness_ebu128.integrated', 'lowlevel.loudness_ebu128.loudness_range', 'lowlevel.loudness_ebu128.momentary.mean', 'lowlevel.loudness_ebu128.momentary.stdev', 'lowlevel.loudness_ebu128.short_term.mean', 'lowlevel.loudness_ebu128.short_term.stdev', 'lowlevel.melbands_crest.mean', 'lowlevel.melbands_crest.stdev', 'lowlevel.melbands_flatness_db.mean', 'lowlevel.melbands_flatness_db.stdev', 'lowlevel.melbands_kurtosis.mean', 'lowlevel.melbands_kurtosis.stdev', 'lowlevel.melbands_skewness.mean', 'lowlevel.melbands_skewness.stdev', 'lowlevel.melbands_spread.mean', 'lowlevel.melbands_spread.stdev', 'lowlevel.pitch_salience.mean', 'lowlevel.pitch_salience.stdev', 'lowlevel.silence_rate_20dB.mean', 'lowlevel.silence_rate_20dB.stdev', 'lowlevel.silence_rate_30dB.mean', 'lowlevel.silence_rate_30dB.stdev', 'lowlevel.silence_rate_60dB.mean', 'lowlevel.silence_rate_60dB.stdev', 'lowlevel.spectral_centroid.mean', 'lowlevel.spectral_centroid.stdev', 'lowlevel.spectral_complexity.mean', 'lowlevel.spectral_complexity.stdev', 'lowlevel.spectral_decrease.mean', 'lowlevel.spectral_decrease.stdev', 'lowlevel.spectral_energy.mean', 'lowlevel.spectral_energy.stdev', 'lowlevel.spectral_energyband_high.mean', 'lowlevel.spectral_energyband_high.stdev', 'lowlevel.spectral_energyband_low.mean', 'lowlevel.spectral_energyband_low.stdev', 'lowlevel.spectral_energyband_middle_high.mean', 'lowlevel.spectral_energyband_middle_high.stdev', 'lowlevel.spectral_energyband_middle_low.mean', 'lowlevel.spectral_energyband_middle_low.stdev', 'lowlevel.spectral_entropy.mean', 'lowlevel.spectral_entropy.stdev', 'lowlevel.spectral_flux.mean', 'lowlevel.spectral_flux.stdev', 'lowlevel.spectral_kurtosis.mean', 'lowlevel.spectral_kurtosis.stdev', 'lowlevel.spectral_rms.mean', 'lowlevel.spectral_rms.stdev', 'lowlevel.spectral_rolloff.mean', 'lowlevel.spectral_rolloff.stdev', 'lowlevel.spectral_skewness.mean', 'lowlevel.spectral_skewness.stdev', 'lowlevel.spectral_spread.mean', 'lowlevel.spectral_spread.stdev', 'lowlevel.spectral_strongpeak.mean', 'lowlevel.spectral_strongpeak.stdev', 'lowlevel.zerocrossingrate.mean', 'lowlevel.zerocrossingrate.stdev']\n"
     ]
    }
   ],
   "source": [
    "scalar_lowlevel_descriptors = [descriptor for descriptor in features.descriptorNames() if 'lowlevel' in descriptor and isinstance(features[descriptor], float)]\n",
    "print(\"Subset of features to be considered:\\n\",scalar_lowlevel_descriptors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o5aBdpBtZ3Cv"
   },
   "source": [
    "Running musicextractor for all files, keeping a subset of features, writing to an output file: data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "POoJ0K8BZ3Cz",
    "outputId": "fd53a71c-1a0d-4886-e6d7-a11e5c8431d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 files processed, current file:  instrument/segments/flute_19.wav\n",
      "40 files processed, current file:  instrument/segments/Violin_3.wav\n",
      "60 files processed, current file:  instrument/segments/Violin_23.wav\n",
      "80 files processed, current file:  instrument/segments/Violin_43.wav\n",
      "100 files processed, current file:  instrument/segments/Violin_63.wav\n",
      "120 files processed, current file:  instrument/segments/EbClar_7.wav\n",
      "140 files processed, current file:  instrument/segments/EbClar_27.wav\n",
      "160 files processed, current file:  instrument/segments/Vibraphone_8.wav\n",
      "180 files processed, current file:  instrument/segments/Vibraphone_28.wav\n",
      "A total of  193 files processed\n"
     ]
    }
   ],
   "source": [
    "#Extracting features and writing in data.csv file in the segments folder\n",
    "#  each line in the data.csv file represents a sample with features and the class information as the last element\n",
    "data_file = os.path.join(segments_dir,'data.csv')\n",
    "file_count = 0\n",
    "with open(data_file, 'w') as writer:\n",
    "    #adding column names as the first line in csv\n",
    "    line2write = ','.join(scalar_lowlevel_descriptors + ['instrument']).replace('lowlevel.','') + '\\n'\n",
    "    writer.write(line2write)\n",
    "    for file in segment_files:\n",
    "        if '.wav' in file:\n",
    "            file_count +=1\n",
    "            if file_count % 20 == 0:#print name of a file every 20 files\n",
    "                print(file_count, \"files processed, current file: \",file)\n",
    "            features, features_frames = es.MusicExtractor(lowlevelSilentFrames='drop',\n",
    "                                                          lowlevelFrameSize = 2048,\n",
    "                                                          lowlevelHopSize = 1024,\n",
    "                                                          lowlevelStats = ['mean', 'stdev'])(file)\n",
    "            selected_features = [features[descriptor] for descriptor in scalar_lowlevel_descriptors]\n",
    "            instrument = file.split('/')[-1].split('_')[0].lower()#class information\n",
    "            line2write = str(selected_features)[1:-1] + ',' + instrument + '\\n'\n",
    "            writer.write(line2write)\n",
    "print(\"A total of \",file_count, \"files processed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mMcx5G3jZ3C3"
   },
   "source": [
    "## Analysis and preprocessing of features\n",
    "\n",
    "All extracted features are saved in data.csv. Columns represent features and rows files/samples. This is a typical format in a large number of machine learning datasets (see a few of the dataset here: https://archive.ics.uci.edu/ml/index.php or other \"Data Science\" examples on the web).  \n",
    "\n",
    "***The use of a common format for data representation is beneficial in both making use of existing tools/libraries (such as Pandas) and contributing to the community with resources that are easy to use.***\n",
    "\n",
    "Let's load the data and start investigating the features. We can use Pandas (a package largely used in data science) to read data and access statistical description of the features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dV84IY-RZ3C4"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "colab_type": "code",
    "id": "CaJDBafwZ3C-",
    "outputId": "96f1c235-4ce7-4273-f18e-38681f2d3d96"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_loudness</th>\n",
       "      <th>barkbands_crest.mean</th>\n",
       "      <th>barkbands_crest.stdev</th>\n",
       "      <th>barkbands_flatness_db.mean</th>\n",
       "      <th>barkbands_flatness_db.stdev</th>\n",
       "      <th>barkbands_kurtosis.mean</th>\n",
       "      <th>barkbands_kurtosis.stdev</th>\n",
       "      <th>barkbands_skewness.mean</th>\n",
       "      <th>barkbands_skewness.stdev</th>\n",
       "      <th>barkbands_spread.mean</th>\n",
       "      <th>...</th>\n",
       "      <th>spectral_rolloff.stdev</th>\n",
       "      <th>spectral_skewness.mean</th>\n",
       "      <th>spectral_skewness.stdev</th>\n",
       "      <th>spectral_spread.mean</th>\n",
       "      <th>spectral_spread.stdev</th>\n",
       "      <th>spectral_strongpeak.mean</th>\n",
       "      <th>spectral_strongpeak.stdev</th>\n",
       "      <th>zerocrossingrate.mean</th>\n",
       "      <th>zerocrossingrate.stdev</th>\n",
       "      <th>instrument</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.983643</td>\n",
       "      <td>10.695081</td>\n",
       "      <td>3.093623</td>\n",
       "      <td>0.284252</td>\n",
       "      <td>0.030590</td>\n",
       "      <td>6.423648</td>\n",
       "      <td>33.268402</td>\n",
       "      <td>0.652544</td>\n",
       "      <td>2.245383</td>\n",
       "      <td>7.373251</td>\n",
       "      <td>...</td>\n",
       "      <td>372.670990</td>\n",
       "      <td>3.449819</td>\n",
       "      <td>0.764383</td>\n",
       "      <td>2964977.75</td>\n",
       "      <td>2749254.750</td>\n",
       "      <td>3.778941</td>\n",
       "      <td>1.780426</td>\n",
       "      <td>0.045833</td>\n",
       "      <td>0.015400</td>\n",
       "      <td>flute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.984904</td>\n",
       "      <td>9.818579</td>\n",
       "      <td>4.676863</td>\n",
       "      <td>0.273402</td>\n",
       "      <td>0.051530</td>\n",
       "      <td>11.307189</td>\n",
       "      <td>59.409706</td>\n",
       "      <td>0.644923</td>\n",
       "      <td>1.531009</td>\n",
       "      <td>8.016563</td>\n",
       "      <td>...</td>\n",
       "      <td>507.695312</td>\n",
       "      <td>3.416012</td>\n",
       "      <td>0.842737</td>\n",
       "      <td>2745592.00</td>\n",
       "      <td>2363405.500</td>\n",
       "      <td>3.718392</td>\n",
       "      <td>1.967154</td>\n",
       "      <td>0.047130</td>\n",
       "      <td>0.014103</td>\n",
       "      <td>flute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.984477</td>\n",
       "      <td>13.813996</td>\n",
       "      <td>3.419898</td>\n",
       "      <td>0.347974</td>\n",
       "      <td>0.055732</td>\n",
       "      <td>3.114700</td>\n",
       "      <td>10.165995</td>\n",
       "      <td>0.499279</td>\n",
       "      <td>1.576066</td>\n",
       "      <td>7.133000</td>\n",
       "      <td>...</td>\n",
       "      <td>376.031403</td>\n",
       "      <td>3.499996</td>\n",
       "      <td>0.749527</td>\n",
       "      <td>2974478.25</td>\n",
       "      <td>1934498.625</td>\n",
       "      <td>4.341050</td>\n",
       "      <td>2.047632</td>\n",
       "      <td>0.047493</td>\n",
       "      <td>0.011993</td>\n",
       "      <td>flute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.981434</td>\n",
       "      <td>14.122767</td>\n",
       "      <td>2.968425</td>\n",
       "      <td>0.361050</td>\n",
       "      <td>0.059990</td>\n",
       "      <td>2.442212</td>\n",
       "      <td>5.224267</td>\n",
       "      <td>0.440628</td>\n",
       "      <td>1.127581</td>\n",
       "      <td>6.965141</td>\n",
       "      <td>...</td>\n",
       "      <td>336.305298</td>\n",
       "      <td>3.592039</td>\n",
       "      <td>0.721372</td>\n",
       "      <td>2892060.00</td>\n",
       "      <td>2223667.750</td>\n",
       "      <td>4.862746</td>\n",
       "      <td>1.768966</td>\n",
       "      <td>0.049851</td>\n",
       "      <td>0.013295</td>\n",
       "      <td>flute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.984899</td>\n",
       "      <td>15.437609</td>\n",
       "      <td>4.146167</td>\n",
       "      <td>0.339545</td>\n",
       "      <td>0.061420</td>\n",
       "      <td>15.572101</td>\n",
       "      <td>78.229233</td>\n",
       "      <td>0.822826</td>\n",
       "      <td>2.659276</td>\n",
       "      <td>7.350607</td>\n",
       "      <td>...</td>\n",
       "      <td>388.273285</td>\n",
       "      <td>3.532580</td>\n",
       "      <td>1.029593</td>\n",
       "      <td>3345785.00</td>\n",
       "      <td>2360139.000</td>\n",
       "      <td>4.882242</td>\n",
       "      <td>2.643430</td>\n",
       "      <td>0.038764</td>\n",
       "      <td>0.011608</td>\n",
       "      <td>flute</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_loudness  barkbands_crest.mean  barkbands_crest.stdev  \\\n",
       "0          0.983643             10.695081               3.093623   \n",
       "1          0.984904              9.818579               4.676863   \n",
       "2          0.984477             13.813996               3.419898   \n",
       "3          0.981434             14.122767               2.968425   \n",
       "4          0.984899             15.437609               4.146167   \n",
       "\n",
       "   barkbands_flatness_db.mean  barkbands_flatness_db.stdev  \\\n",
       "0                    0.284252                     0.030590   \n",
       "1                    0.273402                     0.051530   \n",
       "2                    0.347974                     0.055732   \n",
       "3                    0.361050                     0.059990   \n",
       "4                    0.339545                     0.061420   \n",
       "\n",
       "   barkbands_kurtosis.mean  barkbands_kurtosis.stdev  barkbands_skewness.mean  \\\n",
       "0                 6.423648                 33.268402                 0.652544   \n",
       "1                11.307189                 59.409706                 0.644923   \n",
       "2                 3.114700                 10.165995                 0.499279   \n",
       "3                 2.442212                  5.224267                 0.440628   \n",
       "4                15.572101                 78.229233                 0.822826   \n",
       "\n",
       "   barkbands_skewness.stdev  barkbands_spread.mean  ...  \\\n",
       "0                  2.245383               7.373251  ...   \n",
       "1                  1.531009               8.016563  ...   \n",
       "2                  1.576066               7.133000  ...   \n",
       "3                  1.127581               6.965141  ...   \n",
       "4                  2.659276               7.350607  ...   \n",
       "\n",
       "   spectral_rolloff.stdev  spectral_skewness.mean  spectral_skewness.stdev  \\\n",
       "0              372.670990                3.449819                 0.764383   \n",
       "1              507.695312                3.416012                 0.842737   \n",
       "2              376.031403                3.499996                 0.749527   \n",
       "3              336.305298                3.592039                 0.721372   \n",
       "4              388.273285                3.532580                 1.029593   \n",
       "\n",
       "   spectral_spread.mean  spectral_spread.stdev  spectral_strongpeak.mean  \\\n",
       "0            2964977.75            2749254.750                  3.778941   \n",
       "1            2745592.00            2363405.500                  3.718392   \n",
       "2            2974478.25            1934498.625                  4.341050   \n",
       "3            2892060.00            2223667.750                  4.862746   \n",
       "4            3345785.00            2360139.000                  4.882242   \n",
       "\n",
       "   spectral_strongpeak.stdev  zerocrossingrate.mean  zerocrossingrate.stdev  \\\n",
       "0                   1.780426               0.045833                0.015400   \n",
       "1                   1.967154               0.047130                0.014103   \n",
       "2                   2.047632               0.047493                0.011993   \n",
       "3                   1.768966               0.049851                0.013295   \n",
       "4                   2.643430               0.038764                0.011608   \n",
       "\n",
       "   instrument  \n",
       "0       flute  \n",
       "1       flute  \n",
       "2       flute  \n",
       "3       flute  \n",
       "4       flute  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read data\n",
    "data = pd.read_csv(data_file)\n",
    "#Let's see the first lines of our data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZvAwtqsxZ3DD"
   },
   "source": [
    "All features are represented with columns, and the last column ('instrument') carries the class information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XEnUkMmxZ3DF"
   },
   "source": [
    "Always check if your data involves NaN values and clean them (by simply removing the sample, or replacing the NaN value by a real value (such as the median of that feature))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "thpa7ESXZ3DG",
    "outputId": "f0d89c65-8400-47c7-99b8-c0604b462e51"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum().sum()#sums a matrix of True/False values obtained by checking if each value is Nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "E34fOMf4Z3DN"
   },
   "source": [
    "**Observation:** No NaN value, proceed ..\n",
    "\n",
    "We can plot samples on two-dimensional feature spaces to check if features are discriminative for those classes. Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_zrFi7BLMhai",
    "outputId": "916d739b-dabe-41fd-c3c9-9b13c7596114"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 452.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.relplot(x = \"melbands_flatness_db.mean\", y = \"spectral_centroid.mean\", hue = \"instrument\", style = \"instrument\", data = data);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iO3pP2FOMhan"
   },
   "source": [
    "The Seaborn package includes some very useful plotting functions that facilitate such visual inspections. Here is another example: this time producing plots **for the last 6 features**, creating all combinations to form 2-dimensional feature spaces representing the samples from different classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eYIDJQZfMhap",
    "outputId": "d464e659-b838-4aee-c532-3c03f4f6a676"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1172.38x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data.iloc[:, -7:], hue = \"instrument\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ue9tdBgvMhas"
   },
   "source": [
    "As the next step, having a look at descriptive statistics of the features would be useful. Let's do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 346
    },
    "colab_type": "code",
    "id": "4q8SmhSMZ3DP",
    "outputId": "dd3b5baa-d301-4b4c-cdba-03725281b52b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_loudness</th>\n",
       "      <th>barkbands_crest.mean</th>\n",
       "      <th>barkbands_crest.stdev</th>\n",
       "      <th>barkbands_flatness_db.mean</th>\n",
       "      <th>barkbands_flatness_db.stdev</th>\n",
       "      <th>barkbands_kurtosis.mean</th>\n",
       "      <th>barkbands_kurtosis.stdev</th>\n",
       "      <th>barkbands_skewness.mean</th>\n",
       "      <th>barkbands_skewness.stdev</th>\n",
       "      <th>barkbands_spread.mean</th>\n",
       "      <th>...</th>\n",
       "      <th>spectral_rolloff.mean</th>\n",
       "      <th>spectral_rolloff.stdev</th>\n",
       "      <th>spectral_skewness.mean</th>\n",
       "      <th>spectral_skewness.stdev</th>\n",
       "      <th>spectral_spread.mean</th>\n",
       "      <th>spectral_spread.stdev</th>\n",
       "      <th>spectral_strongpeak.mean</th>\n",
       "      <th>spectral_strongpeak.stdev</th>\n",
       "      <th>zerocrossingrate.mean</th>\n",
       "      <th>zerocrossingrate.stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>1.930000e+02</td>\n",
       "      <td>1.930000e+02</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.986378</td>\n",
       "      <td>18.743959</td>\n",
       "      <td>3.518458</td>\n",
       "      <td>0.430737</td>\n",
       "      <td>0.101979</td>\n",
       "      <td>283.969062</td>\n",
       "      <td>628.854839</td>\n",
       "      <td>3.631706</td>\n",
       "      <td>4.463830</td>\n",
       "      <td>9.592145</td>\n",
       "      <td>...</td>\n",
       "      <td>1538.316643</td>\n",
       "      <td>743.481932</td>\n",
       "      <td>3.356400</td>\n",
       "      <td>1.284691</td>\n",
       "      <td>3.013008e+06</td>\n",
       "      <td>2.337647e+06</td>\n",
       "      <td>3.838570</td>\n",
       "      <td>2.346851</td>\n",
       "      <td>0.055362</td>\n",
       "      <td>0.022823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.006414</td>\n",
       "      <td>4.677126</td>\n",
       "      <td>1.216475</td>\n",
       "      <td>0.079324</td>\n",
       "      <td>0.040225</td>\n",
       "      <td>999.683727</td>\n",
       "      <td>2476.952085</td>\n",
       "      <td>6.139123</td>\n",
       "      <td>7.893363</td>\n",
       "      <td>6.046739</td>\n",
       "      <td>...</td>\n",
       "      <td>712.563348</td>\n",
       "      <td>557.648871</td>\n",
       "      <td>1.771691</td>\n",
       "      <td>0.903258</td>\n",
       "      <td>7.565234e+05</td>\n",
       "      <td>1.193171e+06</td>\n",
       "      <td>2.398980</td>\n",
       "      <td>1.486432</td>\n",
       "      <td>0.023844</td>\n",
       "      <td>0.015404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.944595</td>\n",
       "      <td>7.361399</td>\n",
       "      <td>1.001104</td>\n",
       "      <td>0.260509</td>\n",
       "      <td>0.030590</td>\n",
       "      <td>-1.194350</td>\n",
       "      <td>0.525588</td>\n",
       "      <td>-7.482046</td>\n",
       "      <td>0.199631</td>\n",
       "      <td>0.358621</td>\n",
       "      <td>...</td>\n",
       "      <td>448.608398</td>\n",
       "      <td>5.137959</td>\n",
       "      <td>0.950114</td>\n",
       "      <td>0.320256</td>\n",
       "      <td>1.384232e+06</td>\n",
       "      <td>3.002176e+05</td>\n",
       "      <td>0.805182</td>\n",
       "      <td>0.336106</td>\n",
       "      <td>0.017554</td>\n",
       "      <td>0.004949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.982277</td>\n",
       "      <td>15.009151</td>\n",
       "      <td>2.775063</td>\n",
       "      <td>0.367775</td>\n",
       "      <td>0.072184</td>\n",
       "      <td>5.696008</td>\n",
       "      <td>11.835285</td>\n",
       "      <td>0.806964</td>\n",
       "      <td>1.204419</td>\n",
       "      <td>5.553316</td>\n",
       "      <td>...</td>\n",
       "      <td>1003.193909</td>\n",
       "      <td>376.031403</td>\n",
       "      <td>2.298179</td>\n",
       "      <td>0.661955</td>\n",
       "      <td>2.547992e+06</td>\n",
       "      <td>1.493472e+06</td>\n",
       "      <td>1.990636</td>\n",
       "      <td>1.165608</td>\n",
       "      <td>0.038692</td>\n",
       "      <td>0.012656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.990684</td>\n",
       "      <td>18.754087</td>\n",
       "      <td>3.396118</td>\n",
       "      <td>0.422366</td>\n",
       "      <td>0.097667</td>\n",
       "      <td>11.744920</td>\n",
       "      <td>29.479961</td>\n",
       "      <td>1.685290</td>\n",
       "      <td>2.010876</td>\n",
       "      <td>8.556026</td>\n",
       "      <td>...</td>\n",
       "      <td>1373.966919</td>\n",
       "      <td>606.819214</td>\n",
       "      <td>2.784856</td>\n",
       "      <td>0.906000</td>\n",
       "      <td>2.968861e+06</td>\n",
       "      <td>2.178126e+06</td>\n",
       "      <td>3.288788</td>\n",
       "      <td>2.212921</td>\n",
       "      <td>0.051336</td>\n",
       "      <td>0.018860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.990684</td>\n",
       "      <td>22.895193</td>\n",
       "      <td>4.060771</td>\n",
       "      <td>0.489650</td>\n",
       "      <td>0.125228</td>\n",
       "      <td>57.957668</td>\n",
       "      <td>69.372787</td>\n",
       "      <td>3.784955</td>\n",
       "      <td>3.242225</td>\n",
       "      <td>13.196888</td>\n",
       "      <td>...</td>\n",
       "      <td>2060.806641</td>\n",
       "      <td>965.627014</td>\n",
       "      <td>3.639253</td>\n",
       "      <td>1.611955</td>\n",
       "      <td>3.333934e+06</td>\n",
       "      <td>2.749255e+06</td>\n",
       "      <td>5.056057</td>\n",
       "      <td>3.140011</td>\n",
       "      <td>0.067383</td>\n",
       "      <td>0.027325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.990684</td>\n",
       "      <td>26.508205</td>\n",
       "      <td>8.019903</td>\n",
       "      <td>0.636219</td>\n",
       "      <td>0.264137</td>\n",
       "      <td>9113.941406</td>\n",
       "      <td>22528.568359</td>\n",
       "      <td>33.979153</td>\n",
       "      <td>56.715870</td>\n",
       "      <td>26.559134</td>\n",
       "      <td>...</td>\n",
       "      <td>4256.789551</td>\n",
       "      <td>2818.890381</td>\n",
       "      <td>9.847477</td>\n",
       "      <td>5.230999</td>\n",
       "      <td>6.403085e+06</td>\n",
       "      <td>6.047924e+06</td>\n",
       "      <td>11.551766</td>\n",
       "      <td>11.142887</td>\n",
       "      <td>0.162156</td>\n",
       "      <td>0.109800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_loudness  barkbands_crest.mean  barkbands_crest.stdev  \\\n",
       "count        193.000000            193.000000             193.000000   \n",
       "mean           0.986378             18.743959               3.518458   \n",
       "std            0.006414              4.677126               1.216475   \n",
       "min            0.944595              7.361399               1.001104   \n",
       "25%            0.982277             15.009151               2.775063   \n",
       "50%            0.990684             18.754087               3.396118   \n",
       "75%            0.990684             22.895193               4.060771   \n",
       "max            0.990684             26.508205               8.019903   \n",
       "\n",
       "       barkbands_flatness_db.mean  barkbands_flatness_db.stdev  \\\n",
       "count                  193.000000                   193.000000   \n",
       "mean                     0.430737                     0.101979   \n",
       "std                      0.079324                     0.040225   \n",
       "min                      0.260509                     0.030590   \n",
       "25%                      0.367775                     0.072184   \n",
       "50%                      0.422366                     0.097667   \n",
       "75%                      0.489650                     0.125228   \n",
       "max                      0.636219                     0.264137   \n",
       "\n",
       "       barkbands_kurtosis.mean  barkbands_kurtosis.stdev  \\\n",
       "count               193.000000                193.000000   \n",
       "mean                283.969062                628.854839   \n",
       "std                 999.683727               2476.952085   \n",
       "min                  -1.194350                  0.525588   \n",
       "25%                   5.696008                 11.835285   \n",
       "50%                  11.744920                 29.479961   \n",
       "75%                  57.957668                 69.372787   \n",
       "max                9113.941406              22528.568359   \n",
       "\n",
       "       barkbands_skewness.mean  barkbands_skewness.stdev  \\\n",
       "count               193.000000                193.000000   \n",
       "mean                  3.631706                  4.463830   \n",
       "std                   6.139123                  7.893363   \n",
       "min                  -7.482046                  0.199631   \n",
       "25%                   0.806964                  1.204419   \n",
       "50%                   1.685290                  2.010876   \n",
       "75%                   3.784955                  3.242225   \n",
       "max                  33.979153                 56.715870   \n",
       "\n",
       "       barkbands_spread.mean  ...  spectral_rolloff.mean  \\\n",
       "count             193.000000  ...             193.000000   \n",
       "mean                9.592145  ...            1538.316643   \n",
       "std                 6.046739  ...             712.563348   \n",
       "min                 0.358621  ...             448.608398   \n",
       "25%                 5.553316  ...            1003.193909   \n",
       "50%                 8.556026  ...            1373.966919   \n",
       "75%                13.196888  ...            2060.806641   \n",
       "max                26.559134  ...            4256.789551   \n",
       "\n",
       "       spectral_rolloff.stdev  spectral_skewness.mean  \\\n",
       "count              193.000000              193.000000   \n",
       "mean               743.481932                3.356400   \n",
       "std                557.648871                1.771691   \n",
       "min                  5.137959                0.950114   \n",
       "25%                376.031403                2.298179   \n",
       "50%                606.819214                2.784856   \n",
       "75%                965.627014                3.639253   \n",
       "max               2818.890381                9.847477   \n",
       "\n",
       "       spectral_skewness.stdev  spectral_spread.mean  spectral_spread.stdev  \\\n",
       "count               193.000000          1.930000e+02           1.930000e+02   \n",
       "mean                  1.284691          3.013008e+06           2.337647e+06   \n",
       "std                   0.903258          7.565234e+05           1.193171e+06   \n",
       "min                   0.320256          1.384232e+06           3.002176e+05   \n",
       "25%                   0.661955          2.547992e+06           1.493472e+06   \n",
       "50%                   0.906000          2.968861e+06           2.178126e+06   \n",
       "75%                   1.611955          3.333934e+06           2.749255e+06   \n",
       "max                   5.230999          6.403085e+06           6.047924e+06   \n",
       "\n",
       "       spectral_strongpeak.mean  spectral_strongpeak.stdev  \\\n",
       "count                193.000000                 193.000000   \n",
       "mean                   3.838570                   2.346851   \n",
       "std                    2.398980                   1.486432   \n",
       "min                    0.805182                   0.336106   \n",
       "25%                    1.990636                   1.165608   \n",
       "50%                    3.288788                   2.212921   \n",
       "75%                    5.056057                   3.140011   \n",
       "max                   11.551766                  11.142887   \n",
       "\n",
       "       zerocrossingrate.mean  zerocrossingrate.stdev  \n",
       "count             193.000000              193.000000  \n",
       "mean                0.055362                0.022823  \n",
       "std                 0.023844                0.015404  \n",
       "min                 0.017554                0.004949  \n",
       "25%                 0.038692                0.012656  \n",
       "50%                 0.051336                0.018860  \n",
       "75%                 0.067383                0.027325  \n",
       "max                 0.162156                0.109800  \n",
       "\n",
       "[8 rows x 84 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Descriptive statistics of the features:\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dU9u2YaDZ3DV"
   },
   "source": [
    "**Observation:** min, max, mean values of the features vary a lot, we need to normalise them. Some of the features (such as average loudness) may be irrelevant for the task (check variance of the features). For simplicity of the code here, we will keep all the features as is, but you should consider removing irrelevant features from the list.\n",
    "\n",
    "### Preprocessing of the features\n",
    "\n",
    "**Normalisation of the features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "amcLwVPkZ3DX"
   },
   "outputs": [],
   "source": [
    "data_modif = data.copy()\n",
    "#Let's use sklearn's preprocessing tools for applying normalisation to features\n",
    "from sklearn import preprocessing\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "data_modif.iloc[:,:84] = min_max_scaler.fit_transform(data.iloc[:,:84].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 346
    },
    "colab_type": "code",
    "id": "a8-ErcHzZ3Db",
    "outputId": "50a567f2-f5ab-4234-a57f-1d59f2ca79a1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_loudness</th>\n",
       "      <th>barkbands_crest.mean</th>\n",
       "      <th>barkbands_crest.stdev</th>\n",
       "      <th>barkbands_flatness_db.mean</th>\n",
       "      <th>barkbands_flatness_db.stdev</th>\n",
       "      <th>barkbands_kurtosis.mean</th>\n",
       "      <th>barkbands_kurtosis.stdev</th>\n",
       "      <th>barkbands_skewness.mean</th>\n",
       "      <th>barkbands_skewness.stdev</th>\n",
       "      <th>barkbands_spread.mean</th>\n",
       "      <th>...</th>\n",
       "      <th>spectral_rolloff.mean</th>\n",
       "      <th>spectral_rolloff.stdev</th>\n",
       "      <th>spectral_skewness.mean</th>\n",
       "      <th>spectral_skewness.stdev</th>\n",
       "      <th>spectral_spread.mean</th>\n",
       "      <th>spectral_spread.stdev</th>\n",
       "      <th>spectral_strongpeak.mean</th>\n",
       "      <th>spectral_strongpeak.stdev</th>\n",
       "      <th>zerocrossingrate.mean</th>\n",
       "      <th>zerocrossingrate.stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.906580</td>\n",
       "      <td>0.594489</td>\n",
       "      <td>0.358659</td>\n",
       "      <td>0.453085</td>\n",
       "      <td>0.305674</td>\n",
       "      <td>0.031285</td>\n",
       "      <td>0.027891</td>\n",
       "      <td>0.268052</td>\n",
       "      <td>0.075451</td>\n",
       "      <td>0.352418</td>\n",
       "      <td>...</td>\n",
       "      <td>0.286149</td>\n",
       "      <td>0.262405</td>\n",
       "      <td>0.270449</td>\n",
       "      <td>0.196393</td>\n",
       "      <td>0.324531</td>\n",
       "      <td>0.354477</td>\n",
       "      <td>0.282265</td>\n",
       "      <td>0.186063</td>\n",
       "      <td>0.261462</td>\n",
       "      <td>0.170472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.139165</td>\n",
       "      <td>0.244277</td>\n",
       "      <td>0.173317</td>\n",
       "      <td>0.211131</td>\n",
       "      <td>0.172236</td>\n",
       "      <td>0.109673</td>\n",
       "      <td>0.109950</td>\n",
       "      <td>0.148069</td>\n",
       "      <td>0.139665</td>\n",
       "      <td>0.230787</td>\n",
       "      <td>...</td>\n",
       "      <td>0.187114</td>\n",
       "      <td>0.198187</td>\n",
       "      <td>0.199125</td>\n",
       "      <td>0.183935</td>\n",
       "      <td>0.150736</td>\n",
       "      <td>0.207591</td>\n",
       "      <td>0.223232</td>\n",
       "      <td>0.137546</td>\n",
       "      <td>0.164896</td>\n",
       "      <td>0.146910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.817586</td>\n",
       "      <td>0.399427</td>\n",
       "      <td>0.252744</td>\n",
       "      <td>0.285502</td>\n",
       "      <td>0.178097</td>\n",
       "      <td>0.000756</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.199922</td>\n",
       "      <td>0.017779</td>\n",
       "      <td>0.198267</td>\n",
       "      <td>...</td>\n",
       "      <td>0.145630</td>\n",
       "      <td>0.131815</td>\n",
       "      <td>0.151513</td>\n",
       "      <td>0.069582</td>\n",
       "      <td>0.231878</td>\n",
       "      <td>0.207605</td>\n",
       "      <td>0.110310</td>\n",
       "      <td>0.076758</td>\n",
       "      <td>0.146183</td>\n",
       "      <td>0.073513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.595018</td>\n",
       "      <td>0.341228</td>\n",
       "      <td>0.430805</td>\n",
       "      <td>0.287208</td>\n",
       "      <td>0.001420</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>0.221106</td>\n",
       "      <td>0.032048</td>\n",
       "      <td>0.312872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.242992</td>\n",
       "      <td>0.213836</td>\n",
       "      <td>0.206212</td>\n",
       "      <td>0.119278</td>\n",
       "      <td>0.315735</td>\n",
       "      <td>0.326723</td>\n",
       "      <td>0.231106</td>\n",
       "      <td>0.173670</td>\n",
       "      <td>0.233621</td>\n",
       "      <td>0.132680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.811299</td>\n",
       "      <td>0.435925</td>\n",
       "      <td>0.609888</td>\n",
       "      <td>0.405220</td>\n",
       "      <td>0.006489</td>\n",
       "      <td>0.003056</td>\n",
       "      <td>0.271748</td>\n",
       "      <td>0.053836</td>\n",
       "      <td>0.490001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.423351</td>\n",
       "      <td>0.341355</td>\n",
       "      <td>0.302240</td>\n",
       "      <td>0.263035</td>\n",
       "      <td>0.388475</td>\n",
       "      <td>0.426090</td>\n",
       "      <td>0.395556</td>\n",
       "      <td>0.259458</td>\n",
       "      <td>0.344594</td>\n",
       "      <td>0.213408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_loudness  barkbands_crest.mean  barkbands_crest.stdev  \\\n",
       "count        193.000000            193.000000             193.000000   \n",
       "mean           0.906580              0.594489               0.358659   \n",
       "std            0.139165              0.244277               0.173317   \n",
       "min            0.000000              0.000000               0.000000   \n",
       "25%            0.817586              0.399427               0.252744   \n",
       "50%            1.000000              0.595018               0.341228   \n",
       "75%            1.000000              0.811299               0.435925   \n",
       "max            1.000000              1.000000               1.000000   \n",
       "\n",
       "       barkbands_flatness_db.mean  barkbands_flatness_db.stdev  \\\n",
       "count                  193.000000                   193.000000   \n",
       "mean                     0.453085                     0.305674   \n",
       "std                      0.211131                     0.172236   \n",
       "min                      0.000000                     0.000000   \n",
       "25%                      0.285502                     0.178097   \n",
       "50%                      0.430805                     0.287208   \n",
       "75%                      0.609888                     0.405220   \n",
       "max                      1.000000                     1.000000   \n",
       "\n",
       "       barkbands_kurtosis.mean  barkbands_kurtosis.stdev  \\\n",
       "count               193.000000                193.000000   \n",
       "mean                  0.031285                  0.027891   \n",
       "std                   0.109673                  0.109950   \n",
       "min                   0.000000                  0.000000   \n",
       "25%                   0.000756                  0.000502   \n",
       "50%                   0.001420                  0.001285   \n",
       "75%                   0.006489                  0.003056   \n",
       "max                   1.000000                  1.000000   \n",
       "\n",
       "       barkbands_skewness.mean  barkbands_skewness.stdev  \\\n",
       "count               193.000000                193.000000   \n",
       "mean                  0.268052                  0.075451   \n",
       "std                   0.148069                  0.139665   \n",
       "min                   0.000000                  0.000000   \n",
       "25%                   0.199922                  0.017779   \n",
       "50%                   0.221106                  0.032048   \n",
       "75%                   0.271748                  0.053836   \n",
       "max                   1.000000                  1.000000   \n",
       "\n",
       "       barkbands_spread.mean  ...  spectral_rolloff.mean  \\\n",
       "count             193.000000  ...             193.000000   \n",
       "mean                0.352418  ...               0.286149   \n",
       "std                 0.230787  ...               0.187114   \n",
       "min                 0.000000  ...               0.000000   \n",
       "25%                 0.198267  ...               0.145630   \n",
       "50%                 0.312872  ...               0.242992   \n",
       "75%                 0.490001  ...               0.423351   \n",
       "max                 1.000000  ...               1.000000   \n",
       "\n",
       "       spectral_rolloff.stdev  spectral_skewness.mean  \\\n",
       "count              193.000000              193.000000   \n",
       "mean                 0.262405                0.270449   \n",
       "std                  0.198187                0.199125   \n",
       "min                  0.000000                0.000000   \n",
       "25%                  0.131815                0.151513   \n",
       "50%                  0.213836                0.206212   \n",
       "75%                  0.341355                0.302240   \n",
       "max                  1.000000                1.000000   \n",
       "\n",
       "       spectral_skewness.stdev  spectral_spread.mean  spectral_spread.stdev  \\\n",
       "count               193.000000            193.000000             193.000000   \n",
       "mean                  0.196393              0.324531               0.354477   \n",
       "std                   0.183935              0.150736               0.207591   \n",
       "min                   0.000000              0.000000               0.000000   \n",
       "25%                   0.069582              0.231878               0.207605   \n",
       "50%                   0.119278              0.315735               0.326723   \n",
       "75%                   0.263035              0.388475               0.426090   \n",
       "max                   1.000000              1.000000               1.000000   \n",
       "\n",
       "       spectral_strongpeak.mean  spectral_strongpeak.stdev  \\\n",
       "count                193.000000                 193.000000   \n",
       "mean                   0.282265                   0.186063   \n",
       "std                    0.223232                   0.137546   \n",
       "min                    0.000000                   0.000000   \n",
       "25%                    0.110310                   0.076758   \n",
       "50%                    0.231106                   0.173670   \n",
       "75%                    0.395556                   0.259458   \n",
       "max                    1.000000                   1.000000   \n",
       "\n",
       "       zerocrossingrate.mean  zerocrossingrate.stdev  \n",
       "count             193.000000              193.000000  \n",
       "mean                0.261462                0.170472  \n",
       "std                 0.164896                0.146910  \n",
       "min                 0.000000                0.000000  \n",
       "25%                 0.146183                0.073513  \n",
       "50%                 0.233621                0.132680  \n",
       "75%                 0.344594                0.213408  \n",
       "max                 1.000000                1.000000  \n",
       "\n",
       "[8 rows x 84 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_modif.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UoEwUPNRZ3Dg"
   },
   "source": [
    "**Observation:** Now the features are normalised in [0,1] range, proceed to converting data into matrices that can be fed into classifiers.\n",
    "\n",
    "**IMPORTANT:** Various other preprocessing may be required based on the nature of your data. Refer to your machine learning course for preprocessing steps. We will also skip discussions on feature selection (another important topic) here and leave it to your machine learning course.\n",
    "\n",
    "We should check if our data is balanced:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "hByJk6RnZ3Dh",
    "outputId": "024b4b18-d32e-4968-d890-b50cc75b132e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "violin        76\n",
       "vibraphone    42\n",
       "ebclar        39\n",
       "flute         36\n",
       "Name: instrument, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_modif.instrument.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uxRmyFlBZ3Dl"
   },
   "source": [
    "**Observation:** our data is imbalanced, ups.... refer to your machine learning course for the problem of working with imbalanced datasets. Here, we will simply throw out some samples to balance our data\n",
    "\n",
    "### Balancing the data\n",
    "Let's simply pick 36 samples randomly from each instrument samples (since the lowest number of samples in a class is 36 (flute))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "vBrUNGyqZ3Dq",
    "outputId": "d4463023-85d3-41e2-ddd8-2317400ab0ba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flute         36\n",
       "ebclar        36\n",
       "violin        36\n",
       "vibraphone    36\n",
       "Name: instrument, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_number = data_modif.instrument.value_counts()['flute']\n",
    "violin_data = data_modif[data_modif.instrument == 'violin'].sample(n = min_number, random_state = 42)\n",
    "vibraphone_data = data_modif[data_modif.instrument == 'vibraphone'].sample(n = min_number)\n",
    "ebclar_data = data_modif[data_modif.instrument == 'ebclar'].sample(n = min_number)\n",
    "flute_data = data_modif[data_modif.instrument == 'flute']\n",
    "#Merging after downsampling\n",
    "data_modif = pd.concat([ebclar_data, flute_data, violin_data, vibraphone_data])\n",
    "#Checking the balance again\n",
    "data_modif.instrument.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o6X3nwzjCIez"
   },
   "source": [
    "## Training an automatic classifier and testing it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tWA0Vqf6Z3Dw"
   },
   "source": [
    "**Preparing data arrays for features (inputs, X) and labels (outputs, y):**\n",
    "\n",
    "Now, we can form the input-output matrices that can be fed into classifiers. You can refer to [the tutorials of the scikit learn package](https://scikit-learn.org/stable/tutorial/index.html) to get familiar with the input-output formats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 391
    },
    "colab_type": "code",
    "id": "agdVdsE6Z3Dz",
    "outputId": "05c22e67-3e92-49d7-8623-82b9e026c3c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features of the first sample:  [  8.17585979e-01   3.99427062e-01   2.48767697e-01   4.37337478e-01\n",
      "   3.10795180e-01   1.52695969e-03   1.15703847e-03   2.47859246e-01\n",
      "   2.47329075e-02   3.33848517e-01   3.77492361e-01   6.63510277e-01\n",
      "   3.66223584e-01   2.50913027e-01   4.51755041e-01   3.38602251e-01\n",
      "   3.99431788e-01   4.84870902e-01   3.80309150e-02   2.09250794e-01\n",
      "   6.33423138e-01   3.27409749e-01   3.81851480e-01   1.40215413e-01\n",
      "   3.82623577e-01   2.22672050e-01   8.05420983e-01   0.00000000e+00\n",
      "   8.60704383e-01   1.31921694e-01   8.22103056e-01   0.00000000e+00\n",
      "   8.16165393e-01   2.05130148e-01   4.80342831e-01   2.98366653e-01\n",
      "   2.24095558e-04   1.15823487e-04   5.89191712e-02   5.11745412e-03\n",
      "   1.45753340e-01   2.00904226e-01   2.85870367e-01   9.48829092e-02\n",
      "   1.00000000e+00   0.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "   1.99859758e-01   7.39401949e-01   2.62757951e-01   3.06296269e-01\n",
      "   2.91517946e-01   2.53658168e-01   5.48938614e-01   1.02003754e-01\n",
      "   4.73427877e-01   1.02779718e-01   4.09378350e-03   1.17995629e-03\n",
      "   5.58171679e-05   1.43148883e-05   7.98222454e-02   5.93072830e-02\n",
      "   7.65334851e-01   6.06418716e-01   4.96111885e-01   5.85011194e-01\n",
      "   3.63769611e-03   9.76664248e-02   8.20682619e-02   4.17552322e-02\n",
      "   7.51156088e-01   3.53983110e-01   7.25450385e-02   1.98055418e-01\n",
      "   2.06211883e-01   6.71180810e-02   3.93336873e-01   2.05978559e-01\n",
      "   2.93807089e-01   1.30268610e-01   1.18757575e-01   2.08148292e-01]\n",
      "Class of the first sample:  0\n"
     ]
    }
   ],
   "source": [
    "#input values put in a matrix, there are 84 features\n",
    "X = data_modif.iloc[:,:84].values \n",
    "#Creating output values\n",
    "data_modif.instrument = pd.Categorical(data_modif.instrument)#convert to categorical data\n",
    "y = np.array(data_modif.instrument.cat.codes) #create label encoded outputs\n",
    "#Print the first sample\n",
    "print(\"Features of the first sample: \", X[0])\n",
    "print(\"Class of the first sample: \", y[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1J5XPkyLZ3D5"
   },
   "source": [
    "Let's check content of the outputs vector "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "Sb5rBkO4Z3D6",
    "outputId": "868bd29b-ab2e-4334-f1ad-fff48ec1bfd7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3\n",
      " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n"
     ]
    }
   ],
   "source": [
    "#All output values: ebclar: 0, flute: 1, vibraphone: 2, violin: 3\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JLXjbpv-Dl93"
   },
   "source": [
    "**Splitting data into train and test subsets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CHS_06ejZ3D-"
   },
   "outputs": [],
   "source": [
    "#Let's split data into test and train sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "8p-JGJrwZ3EC",
    "outputId": "470d43de-04be-4a9f-9c26-2ea401dedd5a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of train features matrix:  (96, 84) , Size of train output vector:  (96,)\n",
      "Size of test features matrix:  (48, 84) , Size of test output vector:  (48,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Size of train features matrix: \",X_train.shape, \", Size of train output vector: \",y_train.shape)\n",
    "print(\"Size of test features matrix: \",X_test.shape, \", Size of test output vector: \",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tr8Z2VxrZ3EE"
   },
   "source": [
    "**Defining the machine learning model and training it**\n",
    "\n",
    "Now we can train a machine learning model. Let's pick a Support Vector Machine (SVM) model and feed our data to train it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "RsoR_KCwZ3EG",
    "outputId": "c38900ce-120f-4993-a005-664a9b813a56"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma=0.14084936800826181,\n",
       "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "clf = svm.SVC(gamma = 1 / (X_train.shape[-1] * X_train.var()))\n",
    "clf.fit(X_train, y_train)                         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pOfWFfcgZ3EJ"
   },
   "source": [
    "**Testing the model**\n",
    "\n",
    "The model is trained. Now, one can perform prediction using the model for a given set of features. If we feed the features of the test set, we expect to find the classes of the test set (true values of it are stored in y_test). We will refer to the output as \"predicted classes (y_pred).\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ny-OedXEZ3EK"
   },
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "ncsEtE4IZ3EN",
    "outputId": "07a0e5eb-823f-411b-8f69-dbce62766034"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True, False,  True, False, False,\n",
       "        True,  True,  True,  True,  True,  True,  True, False,  True,\n",
       "        True,  True, False,  True,  True,  True,  True,  True,  True,\n",
       "       False,  True,  True, False,  True,  True, False,  True,  True,\n",
       "        True,  True,  True,  True,  True, False,  True,  True,  True,\n",
       "        True,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's check for each sample in the test set if prediction matches the true class information\n",
    "y_test == y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "p54ZOy8QZ3EP",
    "outputId": "7bdc3c82-f1b7-4881-85ad-ec411378d37b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.8125\n"
     ]
    }
   ],
   "source": [
    "#Data is balanced, so you can use accuracy as a measure:\n",
    "print(\"accuracy: \", np.sum(y_test == y_pred)/len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8As6TL7CFASc"
   },
   "source": [
    "**Observation:** the accuracy looks good but keep in mind that we have a small dataset and simply obtained a random subset for testing. **We also had an important bias !!:** we had formed our segments set by splitting larger audio files. That means a test segment may be coming from a recording which also provided some train segments. **Any other biases you spotted?**\n",
    "\n",
    "**!!! Be suspicious when you hit over 0.9 in the first run:**\n",
    "Check your code. Check if your test set represents a real life scenario and is completely independent of the train set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gzsrAmr3Z3ET"
   },
   "source": [
    "As the final step, let's also print the **confusion matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "id": "0SfE74fYZ3EU",
    "outputId": "b69d72b3-be54-45b1-ed1d-04f560a8b8ac"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "classes = np.unique(data_modif.instrument)\n",
    "conf_mat = pd.DataFrame(confusion_matrix(y_test, y_pred), columns = classes, index = classes)\n",
    "conf_mat.index.name = 'Actual'\n",
    "conf_mat.columns.name = 'Predicted'\n",
    "plt.figure(figsize = (7, 5))\n",
    "sns.set(font_scale = 1.2)\n",
    "sns.heatmap(conf_mat, cmap = \"Blues\", annot_kws = {\"size\": 12}, annot = True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iJORZSKgZ3EX"
   },
   "source": [
    "**Stand-alone use of a trained model:** \n",
    "\n",
    "Now that we have a trained model, we have the tools to perform automatic instrument classification of isolated notes. Below, you find the code to run classification on audio files. This cell does not aim testing (because it also includes the training samples) but just a demonstration of the predictions of our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "G76vKdmEZ3EY",
    "outputId": "0d7d006b-dcdf-4f1d-d765-fa9b0877ef44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "instrument/segments/flute_0.wav  predicted as  flute\n",
      "instrument/segments/flute_1.wav  predicted as  flute\n",
      "instrument/segments/flute_2.wav  predicted as  flute\n",
      "instrument/segments/flute_3.wav  predicted as  flute\n",
      "instrument/segments/flute_4.wav  predicted as  flute\n",
      "instrument/segments/flute_5.wav  predicted as  flute\n",
      "instrument/segments/flute_6.wav  predicted as  flute\n",
      "instrument/segments/flute_7.wav  predicted as  flute\n",
      "instrument/segments/flute_8.wav  predicted as  flute\n",
      "instrument/segments/flute_9.wav  predicted as  flute\n",
      "instrument/segments/flute_10.wav  predicted as  flute\n",
      "instrument/segments/flute_11.wav  predicted as  flute\n",
      "instrument/segments/flute_12.wav  predicted as  flute\n",
      "instrument/segments/flute_13.wav  predicted as  flute\n",
      "instrument/segments/flute_14.wav  predicted as  flute\n",
      "instrument/segments/flute_15.wav  predicted as  flute\n",
      "instrument/segments/flute_16.wav  predicted as  flute\n",
      "instrument/segments/flute_17.wav  predicted as  flute\n",
      "instrument/segments/flute_18.wav  predicted as  flute\n",
      "instrument/segments/flute_19.wav  predicted as  flute\n",
      "instrument/segments/flute_20.wav  predicted as  flute\n",
      "instrument/segments/flute_21.wav  predicted as  flute\n",
      "instrument/segments/flute_22.wav  predicted as  flute\n",
      "instrument/segments/flute_23.wav  predicted as  flute\n",
      "instrument/segments/flute_24.wav  predicted as  ebclar\n",
      "instrument/segments/flute_25.wav  predicted as  flute\n",
      "instrument/segments/flute_26.wav  predicted as  flute\n",
      "instrument/segments/flute_27.wav  predicted as  flute\n",
      "instrument/segments/flute_28.wav  predicted as  flute\n",
      "instrument/segments/flute_29.wav  predicted as  ebclar\n",
      "instrument/segments/flute_30.wav  predicted as  ebclar\n",
      "instrument/segments/flute_31.wav  predicted as  flute\n",
      "instrument/segments/flute_32.wav  predicted as  flute\n",
      "instrument/segments/flute_33.wav  predicted as  flute\n",
      "instrument/segments/flute_34.wav  predicted as  flute\n",
      "instrument/segments/flute_35.wav  predicted as  flute\n",
      "instrument/segments/Violin_0.wav  predicted as  violin\n",
      "instrument/segments/Violin_1.wav  predicted as  violin\n",
      "instrument/segments/Violin_2.wav  predicted as  violin\n",
      "instrument/segments/Violin_3.wav  predicted as  violin\n",
      "instrument/segments/Violin_4.wav  predicted as  violin\n",
      "instrument/segments/Violin_5.wav  predicted as  violin\n",
      "instrument/segments/Violin_6.wav  predicted as  violin\n",
      "instrument/segments/Violin_7.wav  predicted as  violin\n",
      "instrument/segments/Violin_8.wav  predicted as  violin\n",
      "instrument/segments/Violin_9.wav  predicted as  violin\n",
      "instrument/segments/Violin_10.wav  predicted as  violin\n",
      "instrument/segments/Violin_11.wav  predicted as  violin\n",
      "instrument/segments/Violin_12.wav  predicted as  violin\n",
      "instrument/segments/Violin_13.wav  predicted as  violin\n",
      "instrument/segments/Violin_14.wav  predicted as  violin\n",
      "instrument/segments/Violin_15.wav  predicted as  violin\n",
      "instrument/segments/Violin_16.wav  predicted as  violin\n",
      "instrument/segments/Violin_17.wav  predicted as  violin\n",
      "instrument/segments/Violin_18.wav  predicted as  violin\n",
      "instrument/segments/Violin_19.wav  predicted as  violin\n",
      "instrument/segments/Violin_20.wav  predicted as  violin\n",
      "instrument/segments/Violin_21.wav  predicted as  violin\n",
      "instrument/segments/Violin_22.wav  predicted as  violin\n",
      "instrument/segments/Violin_23.wav  predicted as  violin\n",
      "instrument/segments/Violin_24.wav  predicted as  violin\n",
      "instrument/segments/Violin_25.wav  predicted as  violin\n",
      "instrument/segments/Violin_26.wav  predicted as  violin\n",
      "instrument/segments/Violin_27.wav  predicted as  violin\n",
      "instrument/segments/Violin_28.wav  predicted as  violin\n",
      "instrument/segments/Violin_29.wav  predicted as  violin\n",
      "instrument/segments/Violin_30.wav  predicted as  violin\n",
      "instrument/segments/Violin_31.wav  predicted as  violin\n",
      "instrument/segments/Violin_32.wav  predicted as  violin\n",
      "instrument/segments/Violin_33.wav  predicted as  violin\n",
      "instrument/segments/Violin_34.wav  predicted as  violin\n",
      "instrument/segments/Violin_35.wav  predicted as  violin\n",
      "instrument/segments/Violin_36.wav  predicted as  violin\n",
      "instrument/segments/Violin_37.wav  predicted as  violin\n",
      "instrument/segments/Violin_38.wav  predicted as  violin\n",
      "instrument/segments/Violin_39.wav  predicted as  violin\n",
      "instrument/segments/Violin_40.wav  predicted as  violin\n",
      "instrument/segments/Violin_41.wav  predicted as  violin\n",
      "instrument/segments/Violin_42.wav  predicted as  violin\n",
      "instrument/segments/Violin_43.wav  predicted as  violin\n",
      "instrument/segments/Violin_44.wav  predicted as  violin\n",
      "instrument/segments/Violin_45.wav  predicted as  violin\n",
      "instrument/segments/Violin_46.wav  predicted as  violin\n",
      "instrument/segments/Violin_47.wav  predicted as  violin\n",
      "instrument/segments/Violin_48.wav  predicted as  violin\n",
      "instrument/segments/Violin_49.wav  predicted as  violin\n",
      "instrument/segments/Violin_50.wav  predicted as  violin\n",
      "instrument/segments/Violin_51.wav  predicted as  violin\n",
      "instrument/segments/Violin_52.wav  predicted as  violin\n",
      "instrument/segments/Violin_53.wav  predicted as  violin\n",
      "instrument/segments/Violin_54.wav  predicted as  violin\n",
      "instrument/segments/Violin_55.wav  predicted as  violin\n",
      "instrument/segments/Violin_56.wav  predicted as  violin\n",
      "instrument/segments/Violin_57.wav  predicted as  violin\n",
      "instrument/segments/Violin_58.wav  predicted as  violin\n",
      "instrument/segments/Violin_59.wav  predicted as  violin\n",
      "instrument/segments/Violin_60.wav  predicted as  violin\n",
      "instrument/segments/Violin_61.wav  predicted as  violin\n",
      "instrument/segments/Violin_62.wav  predicted as  violin\n",
      "instrument/segments/Violin_63.wav  predicted as  violin\n",
      "instrument/segments/Violin_64.wav  predicted as  violin\n",
      "instrument/segments/Violin_65.wav  predicted as  violin\n",
      "instrument/segments/Violin_66.wav  predicted as  violin\n",
      "instrument/segments/Violin_67.wav  predicted as  violin\n",
      "instrument/segments/Violin_68.wav  predicted as  violin\n",
      "instrument/segments/Violin_69.wav  predicted as  violin\n",
      "instrument/segments/Violin_70.wav  predicted as  violin\n",
      "instrument/segments/Violin_71.wav  predicted as  violin\n",
      "instrument/segments/Violin_72.wav  predicted as  violin\n",
      "instrument/segments/Violin_73.wav  predicted as  violin\n",
      "instrument/segments/Violin_74.wav  predicted as  violin\n",
      "instrument/segments/Violin_75.wav  predicted as  violin\n",
      "instrument/segments/EbClar_0.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_1.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_2.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_3.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_4.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_5.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_6.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_7.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_8.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_9.wav  predicted as  flute\n",
      "instrument/segments/EbClar_10.wav  predicted as  flute\n",
      "instrument/segments/EbClar_11.wav  predicted as  flute\n",
      "instrument/segments/EbClar_12.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_13.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_14.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_15.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_16.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_17.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_18.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_19.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_20.wav  predicted as  flute\n",
      "instrument/segments/EbClar_21.wav  predicted as  flute\n",
      "instrument/segments/EbClar_22.wav  predicted as  flute\n",
      "instrument/segments/EbClar_23.wav  predicted as  flute\n",
      "instrument/segments/EbClar_24.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_25.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_26.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_27.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_28.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_29.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_30.wav  predicted as  flute\n",
      "instrument/segments/EbClar_31.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_32.wav  predicted as  flute\n",
      "instrument/segments/EbClar_33.wav  predicted as  flute\n",
      "instrument/segments/EbClar_34.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_35.wav  predicted as  ebclar\n",
      "instrument/segments/EbClar_36.wav  predicted as  flute\n",
      "instrument/segments/EbClar_37.wav  predicted as  flute\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "instrument/segments/EbClar_38.wav  predicted as  ebclar\n",
      "instrument/segments/Vibraphone_0.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_1.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_2.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_3.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_4.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_5.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_6.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_7.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_8.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_9.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_10.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_11.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_12.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_13.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_14.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_15.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_16.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_17.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_18.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_19.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_20.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_21.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_22.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_23.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_24.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_25.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_26.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_27.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_28.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_29.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_30.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_31.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_32.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_33.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_34.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_35.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_36.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_37.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_38.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_39.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_40.wav  predicted as  vibraphone\n",
      "instrument/segments/Vibraphone_41.wav  predicted as  vibraphone\n"
     ]
    }
   ],
   "source": [
    "#Class names dictionary\n",
    "classes = { 0: 'ebclar', 1: 'flute', 2: 'vibraphone', 3: 'violin'}\n",
    "\n",
    "#Here is the standalone function to run prediction for a given audio file\n",
    "def predict_instrument(file, selected_feature_names, normalizer, classes, classifier):\n",
    "    '''Predicts the instrument class for the given audio file\n",
    "    '''\n",
    "    #Extract features for a given file\n",
    "    features, features_frames = es.MusicExtractor(lowlevelSilentFrames='drop',\n",
    "                                                      lowlevelFrameSize = 2048,\n",
    "                                                      lowlevelHopSize = 1024,\n",
    "                                                      lowlevelStats = ['mean', 'stdev'])(file)\n",
    "    #Pick the features we have used in our model\n",
    "    selected_features = [features[descriptor] for descriptor in scalar_lowlevel_descriptors]\n",
    "    #Applying learned scaling to the features\n",
    "    scaled_features = min_max_scaler.transform(np.array(selected_features).reshape(1, -1))\n",
    "\n",
    "    #Running prediction\n",
    "    y_pred = classifier.predict(scaled_features)\n",
    "    #Return predicted class \n",
    "    return classes[y_pred[0]]\n",
    "#--------------------------------------------------------------------\n",
    "#Running prediction on individual files\n",
    "for file in segment_files:\n",
    "    instrument_pred = predict_instrument(file, scalar_lowlevel_descriptors, min_max_scaler, classes, clf)\n",
    "\n",
    "    #Printing the output\n",
    "    print(file, \" predicted as \", instrument_pred)\n",
    "\n",
    "#!!! This is not a test (as it also contains training samples) but a simple demonstration \n",
    "# running standalone classifier on all files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3L5po7u2Mhbz"
   },
   "source": [
    "### Comparing classifiers\n",
    "\n",
    "Above, we have carried tests for one type of classifier. It is a common practice to compare performances of various classifiers with various settings. Below, we provide an example of such comparison on a limited set of classifiers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FeBmncj9Mhb0",
    "outputId": "4a182db4-22cc-42bf-e39a-e2ae10ab6383"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN: acc = 0.95, +-0.06,\tvalues: [ 1.    0.92  0.92  0.83  0.92  1.    1.    0.92  1.    1.  ]\n",
      "Linear SVM: acc = 0.97, +-0.04,\tvalues: [ 1.    1.    1.    0.92  0.92  1.    1.    0.92  1.    1.  ]\n",
      "RBF SVM: acc = 0.76, +-0.18,\tvalues: [ 0.92  0.83  0.83  0.33  0.75  0.5   0.83  0.83  0.92  0.83]\n",
      "Gaussian Process: acc = 0.78, +-0.21,\tvalues: [ 1.    0.5   0.92  0.92  1.    0.42  0.67  0.58  0.75  1.  ]\n",
      "Neural Net: acc = 0.97, +-0.04,\tvalues: [ 1.    0.92  0.92  0.92  1.    1.    1.    1.    1.    1.  ]\n",
      "Naive Bayes: acc = 0.82, +-0.10,\tvalues: [ 0.92  0.75  0.75  0.83  0.67  0.92  0.92  0.67  0.92  0.83]\n"
     ]
    }
   ],
   "source": [
    "#Edited version of https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')#suppress warnings\n",
    "#importing various classifiers\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(3),\n",
    "    SVC(kernel=\"linear\", C=0.025),\n",
    "    SVC(gamma = 1 / (X_train.shape[-1] * X_train.var())),\n",
    "    GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
    "    MLPClassifier(alpha=1, max_iter=1000),\n",
    "    GaussianNB()]\n",
    "\n",
    "names = [\"KNN\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\"Neural Net\", \"Naive Bayes\"]\n",
    "\n",
    "#Creating empty list of scores for each classifier, we will append test results to these lists \n",
    "scores = {}\n",
    "for name in names:\n",
    "    scores[name] = []\n",
    "\n",
    "#Let's split our data into test and train\n",
    "X_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size = 0.20, random_state = 1)\n",
    "\n",
    "#Let's run 10 random experiments, collect scores for each classifier \n",
    "num_tests = 10\n",
    "for iteration_number in range(num_tests):\n",
    "    x_train, x_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size = 1/num_tests, random_state = iteration_number)\n",
    "    #normalisation of the data (learned from training data, applied to test data)\n",
    "    scaler = StandardScaler().fit(x_train)\n",
    "    norm_x_train = scaler.transform(x_train)\n",
    "    norm_x_val = scaler.transform(x_val)\n",
    "    \n",
    "    # test over each classifier\n",
    "    for name, clf in zip(names, classifiers):\n",
    "        clf.fit(norm_x_train, y_train)#train\n",
    "        score = clf.score(norm_x_val, y_val)#test\n",
    "        scores[name].append(score)\n",
    "\n",
    "for name, score in scores.items():\n",
    "    print(\"{0}: acc = {1:1.2f}, +-{2:1.2f},\\tvalues: {3}\".format(name, np.mean(score), np.std(score), np.around(score, decimals = 2)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "injD39y2Mhb5"
   },
   "source": [
    "Following this comparison, you can pick the classifier with highest performance, and report its performance on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3jB8xAE-Mhb6",
    "outputId": "2c1ba599-4f60-49b4-bff7-ee65b114e59e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of test samples:  29\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.88      0.93         8\n",
      "           1       1.00      1.00      1.00         8\n",
      "           2       1.00      1.00      1.00         6\n",
      "           3       0.88      1.00      0.93         7\n",
      "\n",
      "    accuracy                           0.97        29\n",
      "   macro avg       0.97      0.97      0.97        29\n",
      "weighted avg       0.97      0.97      0.97        29\n",
      "\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler().fit(X_train_val)\n",
    "norm_x_train_val = scaler.transform(X_train_val)\n",
    "norm_x_test = scaler.transform(X_test)\n",
    "selected_model = SVC(kernel=\"linear\", C=0.025)#Linear SVM performance is high, let's pick that one\n",
    "selected_model.fit(norm_x_train_val,y_train_val)\n",
    "y_pred = selected_model.predict(norm_x_test)\n",
    "print(\"Number of test samples: \", len(y_pred))\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3KlNl7fUMhb9"
   },
   "source": [
    "<img src=https://miro.medium.com/max/974/1*CNWo23_VnUJPXdfrqbiB0g.jpeg width=\"400\">\n",
    "\n",
    "<img src=https://www.oreilly.com/library/view/python-data-analysis/9781785282287/graphics/B04223_10_02.jpg width=\"250\">"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Lecture1_IntroWithACaseStudy.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}