CleanedGroupProject.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4a852031",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated\n",
      "  \"class\": algorithms.Blowfish,\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "import scipy.io.wavfile as wavfile\n",
    "from scipy import signal\n",
    "from scipy.signal import spectrogram\n",
    "from scipy.fftpack import fft\n",
    "from pydub import AudioSegment\n",
    "import os\n",
    "import librosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e1bf3495",
   "metadata": {},
   "outputs": [],
   "source": [
    "dev_path = 'dsl_data/development.csv'\n",
    "eval_path = 'dsl_data/evaluation.csv'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a0d7879",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dataframe(path):\n",
    "    df = pd.read_csv(path)\n",
    "    # Dataframe exploration\n",
    "    print('***** DATAFRAME INFO *****\\n')\n",
    "    print(df.info())\n",
    "    print('\\n\\n***** HEAD *****\\n')\n",
    "    print(df.head())\n",
    "    print('\\n\\n ***** POSSIBLE CLASSIFICATION OUTCOMES *****\\n')\n",
    "    print(df.groupby(['action', 'object']).size())\n",
    "    sampleRates = list()\n",
    "    audios = list()\n",
    "    for i in range(0, df.shape[0]):\n",
    "        audio_path = df['path'].loc[i]\n",
    "        sampleRates.append(get_sample_rate(audio_path))\n",
    "        audios.append(get_audio_data(audio_path))\n",
    "    df['SampleRates'] = sampleRates\n",
    "    df['AudioData'] = audios\n",
    "    print('\\n\\n ***** SAMPLE RATES *****')\n",
    "    print(df.groupby(['SampleRates']).size())\n",
    "    return df\n",
    "\n",
    "def get_sample_rate(filepath):\n",
    "    sr, audio = wavfile.read(filepath)\n",
    "    return sr\n",
    "\n",
    "def get_audio_data(filepath):\n",
    "    sr, audio = wavfile.read(filepath)\n",
    "    return audio\n",
    "\n",
    "\n",
    "def edit_sample_rates(goal_sr):\n",
    "    for index, row in df.iterrows():\n",
    "    # Load the audio file\n",
    "        audio = AudioSegment.from_file(row['path'])\n",
    "    # Check if the sample rate is different from the target sample rate\n",
    "        if audio.frame_rate != goal_sr:\n",
    "            # Convert the sample rate to the target sample rate\n",
    "            audio = audio.set_frame_rate(goal_sr)\n",
    "            # get file format\n",
    "            file_format = os.path.splitext(row['path'])[1][1:]\n",
    "            # Save the converted audio\n",
    "            audio.export(row['path'], format=file_format)\n",
    "    sampleRates = list()\n",
    "    for i in range(0, df.shape[0]):\n",
    "        audio_path = df['path'].loc[i]\n",
    "        sampleRates.append(get_sample_rate(audio_path))\n",
    "    df['SampleRates'] = sampleRates\n",
    "    print(df.groupby(['SampleRates']).size())\n",
    "\n",
    "# definition of functions to represent each audio \n",
    "\n",
    "def plot_spectrogram(audio_data, sr):\n",
    "    frequencies, times, spec = spectrogram(audio_data, fs=sr)\n",
    "    plt.pcolormesh(times, frequencies, 10*np.log10(spec)) #changed the scale to dB\n",
    "    plt.colorbar()\n",
    "    plt.xlabel(\"Time (s)\")\n",
    "    plt.ylabel(\"Frequency (Hz)\")\n",
    "    plt.show()\n",
    "    return spec, len(audio)\n",
    "\n",
    "\n",
    "def plot_time_dimension(audio_data, sr):\n",
    "    audioArray = np.array(audio_data)\n",
    "    duration = len(audio) / sr\n",
    "    # Create the time axis in seconds\n",
    "    time = np.linspace(0, duration, len(audio))\n",
    "    plt.plot(time, audioArray)\n",
    "    plt.xlabel(\"Time (s)\")\n",
    "    plt.ylabel(\"Amplitude\")\n",
    "    plt.show()\n",
    "    \n",
    "def plot_freq_dimension(audio_data, sr):\n",
    "    audioArray = np.array(audio_data)\n",
    "    freqArray = np.abs(fft(audioArray))\n",
    "    decibels = 20 * np.log10(freqArray)\n",
    "    plt.figure(figsize=(10,4))\n",
    "    plt.plot(decibels)\n",
    "    plt.xlabel(\"Frequency (Hz)\")\n",
    "    plt.ylabel(\"Magnitude (dB)\")\n",
    "    plt.show()\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c5ac19ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** DATAFRAME INFO *****\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9854 entries, 0 to 9853\n",
      "Data columns (total 10 columns):\n",
      " #   Column                                 Non-Null Count  Dtype \n",
      "---  ------                                 --------------  ----- \n",
      " 0   Id                                     9854 non-null   int64 \n",
      " 1   path                                   9854 non-null   object\n",
      " 2   speakerId                              9854 non-null   object\n",
      " 3   action                                 9854 non-null   object\n",
      " 4   object                                 9854 non-null   object\n",
      " 5   Self-reported fluency level            9854 non-null   object\n",
      " 6   First Language spoken                  9854 non-null   object\n",
      " 7   Current language used for work/school  9854 non-null   object\n",
      " 8   gender                                 9854 non-null   object\n",
      " 9   ageRange                               9854 non-null   object\n",
      "dtypes: int64(1), object(9)\n",
      "memory usage: 770.0+ KB\n",
      "None\n",
      "\n",
      "\n",
      "***** HEAD *****\n",
      "\n",
      "   Id                                               path         speakerId  \\\n",
      "0   0  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0a312...  2BqVo8kVB2Skwgyb   \n",
      "1   1  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0ee42...  2BqVo8kVB2Skwgyb   \n",
      "2   2  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/1d9f3...  2BqVo8kVB2Skwgyb   \n",
      "3   3  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/269fc...  2BqVo8kVB2Skwgyb   \n",
      "4   4  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/5bbda...  2BqVo8kVB2Skwgyb   \n",
      "\n",
      "            action  object Self-reported fluency level   \\\n",
      "0  change language    none                     advanced   \n",
      "1         activate   music                     advanced   \n",
      "2       deactivate  lights                     advanced   \n",
      "3         increase  volume                     advanced   \n",
      "4         increase  volume                     advanced   \n",
      "\n",
      "     First Language spoken Current language used for work/school  gender  \\\n",
      "0  English (United States)               English (United States)  female   \n",
      "1  English (United States)               English (United States)  female   \n",
      "2  English (United States)               English (United States)  female   \n",
      "3  English (United States)               English (United States)  female   \n",
      "4  English (United States)               English (United States)  female   \n",
      "\n",
      "  ageRange  \n",
      "0    22-40  \n",
      "1    22-40  \n",
      "2    22-40  \n",
      "3    22-40  \n",
      "4    22-40  \n",
      "\n",
      "\n",
      " ***** POSSIBLE CLASSIFICATION OUTCOMES *****\n",
      "\n",
      "action           object\n",
      "activate         music      791\n",
      "change language  none      1113\n",
      "deactivate       lights     552\n",
      "decrease         heat      1189\n",
      "                 volume    2386\n",
      "increase         heat      1209\n",
      "                 volume    2614\n",
      "dtype: int64\n",
      "\n",
      "\n",
      " ***** SAMPLE RATES *****\n",
      "SampleRates\n",
      "16000    9554\n",
      "22050     300\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = load_dataframe(dev_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d6da9ca5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SampleRates\n",
      "16000    9854\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "target_rate = 16000\n",
    "edit_sample_rates(target_rate) #bring all sr's to 16kHz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "28325fb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Id</th>\n",
       "      <th>path</th>\n",
       "      <th>speakerId</th>\n",
       "      <th>action</th>\n",
       "      <th>object</th>\n",
       "      <th>Self-reported fluency level</th>\n",
       "      <th>First Language spoken</th>\n",
       "      <th>Current language used for work/school</th>\n",
       "      <th>gender</th>\n",
       "      <th>ageRange</th>\n",
       "      <th>SampleRates</th>\n",
       "      <th>AudioData</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0a312...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>change language</td>\n",
       "      <td>none</td>\n",
       "      <td>advanced</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>female</td>\n",
       "      <td>22-40</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0ee42...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>activate</td>\n",
       "      <td>music</td>\n",
       "      <td>advanced</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>female</td>\n",
       "      <td>22-40</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/1d9f3...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>deactivate</td>\n",
       "      <td>lights</td>\n",
       "      <td>advanced</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>female</td>\n",
       "      <td>22-40</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/269fc...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>increase</td>\n",
       "      <td>volume</td>\n",
       "      <td>advanced</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>female</td>\n",
       "      <td>22-40</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/5bbda...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>increase</td>\n",
       "      <td>volume</td>\n",
       "      <td>advanced</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>female</td>\n",
       "      <td>22-40</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id                                               path         speakerId  \\\n",
       "0   0  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0a312...  2BqVo8kVB2Skwgyb   \n",
       "1   1  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0ee42...  2BqVo8kVB2Skwgyb   \n",
       "2   2  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/1d9f3...  2BqVo8kVB2Skwgyb   \n",
       "3   3  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/269fc...  2BqVo8kVB2Skwgyb   \n",
       "4   4  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/5bbda...  2BqVo8kVB2Skwgyb   \n",
       "\n",
       "            action  object Self-reported fluency level   \\\n",
       "0  change language    none                     advanced   \n",
       "1         activate   music                     advanced   \n",
       "2       deactivate  lights                     advanced   \n",
       "3         increase  volume                     advanced   \n",
       "4         increase  volume                     advanced   \n",
       "\n",
       "     First Language spoken Current language used for work/school  gender  \\\n",
       "0  English (United States)               English (United States)  female   \n",
       "1  English (United States)               English (United States)  female   \n",
       "2  English (United States)               English (United States)  female   \n",
       "3  English (United States)               English (United States)  female   \n",
       "4  English (United States)               English (United States)  female   \n",
       "\n",
       "  ageRange  SampleRates                                          AudioData  \n",
       "0    22-40        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "1    22-40        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "2    22-40        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "3    22-40        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "4    22-40        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d320c1a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_all_audios(df):\n",
    "    lengths = list()\n",
    "    fig, ax = plt.subplots(1,1)\n",
    "    for ind in range(0, len(df)):\n",
    "        audio = df['AudioData'].loc[ind]\n",
    "        sr = df['SampleRates'].loc[ind]\n",
    "        audioArray = np.array(audio)\n",
    "        duration = len(audio) / sr\n",
    "        time = np.linspace(0, duration, len(audio))\n",
    "        ax.plot(time, audioArray, alpha=0.5)\n",
    "    ax.set_ylabel(\"Amplitude\")\n",
    "    ax.set_xlabel(\"Time [s]\")\n",
    "    plt.show()\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "095c6464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_audios(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "00402076",
   "metadata": {},
   "outputs": [],
   "source": [
    "recList = []\n",
    "lenList = []\n",
    "\n",
    "for i in df[\"path\"]:\n",
    "    #print(i)\n",
    "    \n",
    "    rec, sr = librosa.load(i)\n",
    "    recList.append(rec)\n",
    "    lenList.append(len(rec)/sr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "60cb8872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.,   3.,   0.,   9.,  24.,  43.,  54.,  12.,  89., 169., 176.,\n",
       "        103., 408., 296., 648., 689., 251., 500., 722., 649.,   2., 661.,\n",
       "        540., 514.,   1., 448., 443., 228., 334.,  74., 218., 180., 169.,\n",
       "         34., 114., 121.,  88.,  95.,   0.,  68.,  78.,  29.,   8.,  36.,\n",
       "         33.,  30.,   6.,  18.,  13.,  31.,   9.,   0.,  13.,   8.,   6.,\n",
       "          0.,   6.,  10.,   6.,   6.,   1.,   1.,   1.,   3.,   1.,   2.,\n",
       "          2.,   1.,   1.,   0.,   1.,   1.,   1.,   0.,   0.,   0.,   0.,\n",
       "          0.,   2.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,\n",
       "          1.,   0.,   1.,   0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,\n",
       "          1.,   0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   1.,   0.,   1.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0., 300.]),\n",
       " array([ 0.65020408,  0.71624434,  0.7822846 ,  0.84832486,  0.91436512,\n",
       "         0.98040538,  1.04644564,  1.1124859 ,  1.17852615,  1.24456641,\n",
       "         1.31060667,  1.37664693,  1.44268719,  1.50872745,  1.57476771,\n",
       "         1.64080797,  1.70684823,  1.77288849,  1.83892875,  1.904969  ,\n",
       "         1.97100926,  2.03704952,  2.10308978,  2.16913004,  2.2351703 ,\n",
       "         2.30121056,  2.36725082,  2.43329108,  2.49933134,  2.5653716 ,\n",
       "         2.63141185,  2.69745211,  2.76349237,  2.82953263,  2.89557289,\n",
       "         2.96161315,  3.02765341,  3.09369367,  3.15973393,  3.22577419,\n",
       "         3.29181445,  3.35785471,  3.42389496,  3.48993522,  3.55597548,\n",
       "         3.62201574,  3.688056  ,  3.75409626,  3.82013652,  3.88617678,\n",
       "         3.95221704,  4.0182573 ,  4.08429756,  4.15033781,  4.21637807,\n",
       "         4.28241833,  4.34845859,  4.41449885,  4.48053911,  4.54657937,\n",
       "         4.61261963,  4.67865989,  4.74470015,  4.81074041,  4.87678066,\n",
       "         4.94282092,  5.00886118,  5.07490144,  5.1409417 ,  5.20698196,\n",
       "         5.27302222,  5.33906248,  5.40510274,  5.471143  ,  5.53718326,\n",
       "         5.60322351,  5.66926377,  5.73530403,  5.80134429,  5.86738455,\n",
       "         5.93342481,  5.99946507,  6.06550533,  6.13154559,  6.19758585,\n",
       "         6.26362611,  6.32966636,  6.39570662,  6.46174688,  6.52778714,\n",
       "         6.5938274 ,  6.65986766,  6.72590792,  6.79194818,  6.85798844,\n",
       "         6.9240287 ,  6.99006896,  7.05610922,  7.12214947,  7.18818973,\n",
       "         7.25422999,  7.32027025,  7.38631051,  7.45235077,  7.51839103,\n",
       "         7.58443129,  7.65047155,  7.71651181,  7.78255207,  7.84859232,\n",
       "         7.91463258,  7.98067284,  8.0467131 ,  8.11275336,  8.17879362,\n",
       "         8.24483388,  8.31087414,  8.3769144 ,  8.44295466,  8.50899492,\n",
       "         8.57503517,  8.64107543,  8.70711569,  8.77315595,  8.83919621,\n",
       "         8.90523647,  8.97127673,  9.03731699,  9.10335725,  9.16939751,\n",
       "         9.23543777,  9.30147802,  9.36751828,  9.43355854,  9.4995988 ,\n",
       "         9.56563906,  9.63167932,  9.69771958,  9.76375984,  9.8298001 ,\n",
       "         9.89584036,  9.96188062, 10.02792087, 10.09396113, 10.16000139,\n",
       "        10.22604165, 10.29208191, 10.35812217, 10.42416243, 10.49020269,\n",
       "        10.55624295, 10.62228321, 10.68832347, 10.75436373, 10.82040398,\n",
       "        10.88644424, 10.9524845 , 11.01852476, 11.08456502, 11.15060528,\n",
       "        11.21664554, 11.2826858 , 11.34872606, 11.41476632, 11.48080658,\n",
       "        11.54684683, 11.61288709, 11.67892735, 11.74496761, 11.81100787,\n",
       "        11.87704813, 11.94308839, 12.00912865, 12.07516891, 12.14120917,\n",
       "        12.20724943, 12.27328968, 12.33932994, 12.4053702 , 12.47141046,\n",
       "        12.53745072, 12.60349098, 12.66953124, 12.7355715 , 12.80161176,\n",
       "        12.86765202, 12.93369228, 12.99973253, 13.06577279, 13.13181305,\n",
       "        13.19785331, 13.26389357, 13.32993383, 13.39597409, 13.46201435,\n",
       "        13.52805461, 13.59409487, 13.66013513, 13.72617538, 13.79221564,\n",
       "        13.8582559 , 13.92429616, 13.99033642, 14.05637668, 14.12241694,\n",
       "        14.1884572 , 14.25449746, 14.32053772, 14.38657798, 14.45261824,\n",
       "        14.51865849, 14.58469875, 14.65073901, 14.71677927, 14.78281953,\n",
       "        14.84885979, 14.91490005, 14.98094031, 15.04698057, 15.11302083,\n",
       "        15.17906109, 15.24510134, 15.3111416 , 15.37718186, 15.44322212,\n",
       "        15.50926238, 15.57530264, 15.6413429 , 15.70738316, 15.77342342,\n",
       "        15.83946368, 15.90550394, 15.97154419, 16.03758445, 16.10362471,\n",
       "        16.16966497, 16.23570523, 16.30174549, 16.36778575, 16.43382601,\n",
       "        16.49986627, 16.56590653, 16.63194679, 16.69798704, 16.7640273 ,\n",
       "        16.83006756, 16.89610782, 16.96214808, 17.02818834, 17.0942286 ,\n",
       "        17.16026886, 17.22630912, 17.29234938, 17.35838964, 17.42442989,\n",
       "        17.49047015, 17.55651041, 17.62255067, 17.68859093, 17.75463119,\n",
       "        17.82067145, 17.88671171, 17.95275197, 18.01879223, 18.08483249,\n",
       "        18.15087275, 18.216913  , 18.28295326, 18.34899352, 18.41503378,\n",
       "        18.48107404, 18.5471143 , 18.61315456, 18.67919482, 18.74523508,\n",
       "        18.81127534, 18.8773156 , 18.94335585, 19.00939611, 19.07543637,\n",
       "        19.14147663, 19.20751689, 19.27355715, 19.33959741, 19.40563767,\n",
       "        19.47167793, 19.53771819, 19.60375845, 19.6697987 , 19.73583896,\n",
       "        19.80187922, 19.86791948, 19.93395974, 20.        ]),\n",
       " <BarContainer object of 293 artists>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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66y698soruuuuu3TZZd9np4SEBL344ou69tprvVosAADAxfIo7KxYsULTpk3TI488Irvd3myfXr16KT8/v03FAQAAtJVHYefQoUPn7RMUFKQpU6Z4cngAAACv8eienYKCAr322mtN2l977TWtXLmyzUUBAAB4i0dhZ+HCherevXuT9h49eignJ6fNRQEAAHiLR2Hn8OHDSkhIaNLeu3dvHTlypM1FAQAAeItHYadHjx769NNPm7Tv2bNH0dHRbS4KAADAWzwKOxMnTtTPfvYzbdmyRQ0NDWpoaND777+v2bNna+LEid6uEQAAwGMePY31zDPP6PDhw0pNTVVAwPeHaGxs1OTJk7lnBwAA+BWPwk5QUJD++Mc/6te//rX27Nmj0NBQJScnq3fv3t6uDwAAoE08Cjtn9e3bV3379vVWLQAAAF7nUdhpaGhQYWGh3nvvPVVUVKixsdFt+/vvv++V4gAAANrKoxuUZ8+erdmzZ6uhoUFJSUkaMGCA2+KJ3Nxc2Ww2ZWZmutqMMcrOzlZcXJxCQ0M1YsQI7d+/320/p9OpWbNmqXv37goPD9e4ceN07Ngxj2rwd+npxb4uAQCADsejmZ21a9fqT3/6k+666y6vFLFz50699NJLuuGGG9zaFy1apLy8PBUWFqpv37565plnNGrUKB04cEARERGSpMzMTP35z3/W2rVrFR0draysLI0dO1YlJSXq0qWLV+oDAAAdl0czO0FBQbrmmmu8UsDJkyf10EMP6eWXX1bXrl1d7cYYLV26VAsWLFBGRoaSkpK0cuVKfffdd1qzZo0kqaqqSvn5+Vq8eLFGjhypG2+8UatXr9bevXu1efNmr9QHAAA6No/CTlZWln7729/KGNPmAh5//HHdfffdGjlypFt7WVmZHA6H0tLSXG3BwcEaPny4tm/fLkkqKSlRfX29W5+4uDglJSW5+jTH6XSqurrabQEAANbk0WWs4uJibdmyRW+//bauv/56BQYGum1ft27dBR1n7dq1Kikp0a5du5psczgckqSYmBi39piYGB0+fNjVJygoyG1G6Gyfs/s3Jzc3V08//fQF1QgAADo2j8LOFVdcoXvvvbdNL3z06FHNnj1bmzZtUkhISIv9bDab27oxpknbuc7XZ/78+ZozZ45rvbq6WvHx8RdYOQAA6Eg8CjsFBQVtfuGSkhJVVFQoJSXF1dbQ0KAPPvhAy5cv14EDByR9P3sTGxvr6lNRUeGa7bHb7aqrq1NlZaXb7E5FRYWGDh3a4msHBwcrODi4zWMAAAD+z6N7diTpzJkz2rx5s1588UXV1NRIko4fP66TJ09e0P6pqanau3evdu/e7VoGDRqkhx56SLt379bVV18tu92uoqIi1z51dXXatm2bK8ikpKQoMDDQrU95ebn27dvXatgBAACdh0czO4cPH9add96pI0eOyOl0atSoUYqIiNCiRYt0+vRpvfDCC+c9RkREhJKSktzawsPDFR0d7WrPzMxUTk6OEhMTlZiYqJycHIWFhWnSpEmSpKioKE2fPl1ZWVmKjo5Wt27dNHfuXCUnJze54RkAAHROHoWd2bNna9CgQdqzZ4+io6Nd7ffee68effRRrxU3b9481dbWaubMmaqsrNTgwYO1adMm13fsSNKSJUsUEBCgCRMmqLa2VqmpqSosLOQ7dgAAgKQ2PI314YcfKigoyK29d+/e+vLLLz0uZuvWrW7rNptN2dnZys7ObnGfkJAQLVu2TMuWLfP4dQEAgHV5dM9OY2OjGhoamrQfO3bMbdYFAADA1zwKO6NGjdLSpUtd6zabTSdPntRTTz3ltZ+QAAAA8AaPLmMtWbJEt99+u/r376/Tp09r0qRJOnTokLp3765XX33V2zUCAAB4zKOwExcXp927d+vVV1/VJ598osbGRk2fPl0PPfSQQkNDvV0jAACAxzwKO5IUGhqqadOmadq0ad6sBwAAwKs8CjuvvPJKq9snT57sUTEAAADe5vH37PxQfX29vvvuOwUFBSksLIywAwAA/IZHT2NVVla6LSdPntSBAwd06623coMyAADwKx7/Nta5EhMTtXDhwiazPgAAAL7ktbAjSV26dNHx48e9eUgAAIA28eienY0bN7qtG2NUXl6u5cuX65ZbbvFKYQAAAN7gUdhJT093W7fZbLryyit1xx13aPHixd6oCwAAwCs8CjuNjY3ergMAAKBdePWeHQAAAH/j0czOnDlzLrhvXl6eJy8BAADgFR6FndLSUn3yySc6c+aM+vXrJ0k6ePCgunTpooEDB7r62Ww271QJAADgIY/Czj333KOIiAitXLlSXbt2lfT9Fw0+8sgjuu2225SVleXVIgEAADzl0T07ixcvVm5urivoSFLXrl31zDPP8DQWAADwKx6Fnerqan311VdN2isqKlRTU9PmogAAALzFo7Bz77336pFHHtHrr7+uY8eO6dixY3r99dc1ffp0ZWRkeLtGAAAAj3l0z84LL7yguXPn6ic/+Ynq6+u/P1BAgKZPn67nnnvOqwUCAAC0hUdhJywsTM8//7yee+45ff755zLG6JprrlF4eLi36wMAAGiTNn2pYHl5ucrLy9W3b1+Fh4fLGOOtugAAALzCo7Bz4sQJpaamqm/fvrrrrrtUXl4uSXr00Ud57BwAAPgVj8LOz3/+cwUGBurIkSMKCwtztT/wwAN65513vFYcAABAW3kUdjZt2qRnn31WPXv2dGtPTEzU4cOHvVIYfCM9vdjXJQAA4FUehZ1Tp065zeic9fXXXys4OLjNRQEAAHiLR2Fn2LBheuWVV1zrNptNjY2Neu6553T77bd7rTgAAIC28ujR8+eee04jRozQrl27VFdXp3nz5mn//v365ptv9OGHH3q7RgAAAI95NLPTv39/ffrpp/rxj3+sUaNG6dSpU8rIyFBpaan69Onj7RoBAAA8dtEzO/X19UpLS9OLL76op59+uj1qAgAA8JqLntkJDAzUvn37ZLPZ2qMeAAAAr/LoMtbkyZOVn5/v7VoAAAC8zqMblOvq6vRf//VfKioq0qBBg5r8JlZeXp5XigMAAGiriwo7X3zxha666irt27dPAwcOlCQdPHjQrQ+XtwAAgD+5qLCTmJio8vJybdmyRdL3Pw/xu9/9TjExMe1SHAAAQFtd1D075/6q+dtvv61Tp055tSAAAABv8ugG5bPODT8AAAD+5qLCjs1ma3JPTlvu0VmxYoVuuOEGRUZGKjIyUkOGDNHbb7/t2m6MUXZ2tuLi4hQaGqoRI0Zo//79bsdwOp2aNWuWunfvrvDwcI0bN07Hjh3zuCYAAGAtF3XPjjFGU6dOdf3Y5+nTp/XYY481eRpr3bp1F3S8nj17auHChbrmmmskSStXrtT48eNVWlqq66+/XosWLVJeXp4KCwvVt29fPfPMMxo1apQOHDigiIgISVJmZqb+/Oc/a+3atYqOjlZWVpbGjh2rkpISdenS5WKGBwAALOiiws6UKVPc1n/yk5+06cXvuecet/X//M//1IoVK7Rjxw71799fS5cu1YIFC5SRkSHp+zAUExOjNWvWaMaMGaqqqlJ+fr5WrVqlkSNHSpJWr16t+Ph4bd68WaNHj272dZ1Op5xOp2u9urq6TeMAAAD+66LCTkFBQXvVoYaGBr322ms6deqUhgwZorKyMjkcDqWlpbn6BAcHa/jw4dq+fbtmzJihkpIS189XnBUXF6ekpCRt3769xbCTm5vLT10AAHAJpKcXa8OGW31aQ5tuUPaGvXv36vLLL1dwcLAee+wxrV+/Xv3795fD4ZCkJo+1x8TEuLY5HA4FBQWpa9euLfZpzvz581VVVeVajh496uVRAQAAf+HRNyh7U79+/bR79259++23euONNzRlyhRt27bNtf3cG6CNMee9Kfp8fYKDg133HXU06enFvi4BAIAOxeczO0FBQbrmmms0aNAg5ebmasCAAfrtb38ru90uSU1maCoqKlyzPXa7XXV1daqsrGyxDwAA6Nx8HnbOZYyR0+lUQkKC7Ha7ioqKXNvq6uq0bds2DR06VJKUkpKiwMBAtz7l5eXat2+fqw8AAOjcfHoZ65e//KXGjBmj+Ph41dTUaO3atdq6daveeecd2Ww2ZWZmKicnR4mJiUpMTFROTo7CwsI0adIkSVJUVJSmT5+urKwsRUdHq1u3bpo7d66Sk5NdT2cBAIDOzadh56uvvtLDDz+s8vJyRUVF6YYbbtA777yjUaNGSZLmzZun2tpazZw5U5WVlRo8eLA2bdrk+o4dSVqyZIkCAgI0YcIE1dbWKjU1VYWFhXzHDgAAkOTjsJOfn9/qdpvNpuzsbGVnZ7fYJyQkRMuWLdOyZcu8XB0AALACv7tnBwAAwJsIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNICfF0A2i49vdjXJQAA4LeY2QEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJbGb2NBEr+vBQCwLmZ2AACApRF2AACApRF2AACApRF20CLu4wEAWAFhBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWJpPw05ubq5uuukmRUREqEePHkpPT9eBAwfc+hhjlJ2drbi4OIWGhmrEiBHav3+/Wx+n06lZs2ape/fuCg8P17hx43Ts2LFLORQAAOCnfBp2tm3bpscff1w7duxQUVGRzpw5o7S0NJ06dcrVZ9GiRcrLy9Py5cu1c+dO2e12jRo1SjU1Na4+mZmZWr9+vdauXavi4mKdPHlSY8eOVUNDgy+GBQAA/IhPfxvrnXfecVsvKChQjx49VFJSomHDhskYo6VLl2rBggXKyMiQJK1cuVIxMTFas2aNZsyYoaqqKuXn52vVqlUaOXKkJGn16tWKj4/X5s2bNXr06Cav63Q65XQ6XevV1dXtOEoAAOBLfnXPTlVVlSSpW7dukqSysjI5HA6lpaW5+gQHB2v48OHavn27JKmkpET19fVufeLi4pSUlOTqc67c3FxFRUW5lvj4+PYaEgAA8DG/CTvGGM2ZM0e33nqrkpKSJEkOh0OSFBMT49Y3JibGtc3hcCgoKEhdu3Ztsc+55s+fr6qqKtdy9OhRbw8HAAD4CZ9exvqhJ554Qp9++qmKi5v+RIHNZnNbN8Y0aTtXa32Cg4MVHBzsebEAAKDD8IuZnVmzZmnjxo3asmWLevbs6Wq32+2S1GSGpqKiwjXbY7fbVVdXp8rKyhb7AACAzsunYccYoyeeeELr1q3T+++/r4SEBLftCQkJstvtKioqcrXV1dVp27ZtGjp0qCQpJSVFgYGBbn3Ky8u1b98+Vx8AANB5+fQy1uOPP641a9bof/7nfxQREeGawYmKilJoaKhsNpsyMzOVk5OjxMREJSYmKicnR2FhYZo0aZKr7/Tp05WVlaXo6Gh169ZNc+fOVXJysuvpLAAA0Hn5NOysWLFCkjRixAi39oKCAk2dOlWSNG/ePNXW1mrmzJmqrKzU4MGDtWnTJkVERLj6L1myRAEBAZowYYJqa2uVmpqqwsJCdenS5VINBQAA+Cmfhh1jzHn72Gw2ZWdnKzs7u8U+ISEhWrZsmZYtW+bF6gAAgBX4xQ3KAAAA7YWwAwAALI2wg1alpxcrPb3pdx8BANBREHYAAIClEXb8HLMqAAC0DWEHAABYGmEHAABYGmHHT/nbjcH+VAsAABeDsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAOPpKcXKz292NdlAABwXoQdAABgaYQdAABgaYQdXDQuXwEAOhLCDgAAsDTCDgAAsDSfhp0PPvhA99xzj+Li4mSz2bRhwwa37cYYZWdnKy4uTqGhoRoxYoT279/v1sfpdGrWrFnq3r27wsPDNW7cOB07duwSjgIAAPgzn4adU6dOacCAAVq+fHmz2xctWqS8vDwtX75cO3fulN1u16hRo1RTU+Pqk5mZqfXr12vt2rUqLi7WyZMnNXbsWDU0NFyqYQAAAD8W4MsXHzNmjMaMGdPsNmOMli5dqgULFigjI0OStHLlSsXExGjNmjWaMWOGqqqqlJ+fr1WrVmnkyJGSpNWrVys+Pl6bN2/W6NGjL9lYAACAf/Lbe3bKysrkcDiUlpbmagsODtbw4cO1fft2SVJJSYnq6+vd+sTFxSkpKcnVpzlOp1PV1dVuiz/jC/wAAPCc34Ydh8MhSYqJiXFrj4mJcW1zOBwKCgpS165dW+zTnNzcXEVFRbmW+Ph4L1cPAAD8hd+GnbNsNpvbujGmSdu5ztdn/vz5qqqqci1Hjx71Sq0AAMD/+G3YsdvtktRkhqaiosI122O321VXV6fKysoW+zQnODhYkZGRbgs8x2U2AIA/89uwk5CQILvdrqKiIldbXV2dtm3bpqFDh0qSUlJSFBgY6NanvLxc+/btc/UBAACdm0+fxjp58qT+/ve/u9bLysq0e/dudevWTb169VJmZqZycnKUmJioxMRE5eTkKCwsTJMmTZIkRUVFafr06crKylJ0dLS6deumuXPnKjk52fV0FgAA6Nx8GnZ27dql22+/3bU+Z84cSdKUKVNUWFioefPmqba2VjNnzlRlZaUGDx6sTZs2KSIiwrXPkiVLFBAQoAkTJqi2tlapqakqLCxUly5dLvl4vIVLQgAAeI9Pw86IESNkjGlxu81mU3Z2trKzs1vsExISomXLlmnZsmXtUCHOh2AGAPB3fnvPDgAAgDcQdgAAgKURdgAAgKURdgAAgKURdtAu+KJBAIC/IOwAAABL8+mj5/BPzMgAAKyEmR0AAGBphB0AAGBpXMaCV3EJDADgb5jZAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbY8RM8xQQAQPsg7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEvjt7F8jKewAABoX8zsAAAASyPsAAAASyPs4JLhkh0AwBe4ZwcXjLACAOiImNnBJZWeXkxoAgBcUoQd+ERLgYcgBADwNsIO2hXhBQDga4QdAABgaYQdH2LWAwCA9kfYAQAAlkbYAQAAlkbYgc9xOQ8A0J4IOwAAwNL4BmX4hfN9786GDbdeynIAABZC2PEjXM4BAMD7CDs+QKhp3cXM5jDzAwA4H8vcs/P8888rISFBISEhSklJ0V/+8hdfl4T/xxu/h8VvagEAPGWJsPPHP/5RmZmZWrBggUpLS3XbbbdpzJgxOnLkiK9LQys8CTA/7H/unwlEAIDmWOIyVl5enqZPn65HH31UkrR06VK9++67WrFihXJzc31a27kfvlxuufS41AUAnVuHDzt1dXUqKSnRk08+6daelpam7du3N7uP0+mU0+l0rVdVVUmSqqurvV5fff0pt/Xq6uombe3l7rvfvaj2tvL22Fo63tnz1NJreTruV18dogcf/KjZ9pac7d9an9b2++G+Dz74UZPjNNfW3LF+2MfTmi7mNT09nrePjYvHOcClVl9/ql0+X6X//3lgjGm1X4cPO19//bUaGhoUExPj1h4TEyOHw9HsPrm5uXr66aebtMfHx7dLjT8UFdXuL+Ez3h5bS8drr7/DtrxeW2r64b7NHcfT1/dWTd5wvjHi0uIc4FJr739zNTU1imrlRTp82DnLZrO5rRtjmrSdNX/+fM2ZM8e13tjYqG+++UbR0dFN9qmurlZ8fLyOHj2qyMhI7xfuRzrLWBmntXSWcUqdZ6yM01rac5zGGNXU1CguLq7Vfh0+7HTv3l1dunRpMotTUVHRZLbnrODgYAUHB7u1XXHFFa2+TmRkpKX/Mf5QZxkr47SWzjJOqfOMlXFaS3uNs7UZnbM6/NNYQUFBSklJUVFRkVt7UVGRhg4d6qOqAACAv+jwMzuSNGfOHD388MMaNGiQhgwZopdeeklHjhzRY4895uvSAACAj1ki7DzwwAM6ceKEfvWrX6m8vFxJSUl666231Lt37zYfOzg4WE899VSTy15W1FnGyjitpbOMU+o8Y2Wc1uIP47SZ8z2vBQAA0IF1+Ht2AAAAWkPYAQAAlkbYAQAAlkbYAQAAlkbYkfT8888rISFBISEhSklJ0V/+8pdW+2/btk0pKSkKCQnR1VdfrRdeeOESVeqZ3Nxc3XTTTYqIiFCPHj2Unp6uAwcOtLrP1q1bZbPZmiz/+7//e4mq9kx2dnaTmu12e6v7dLTzKUlXXXVVs+fn8ccfb7Z/RzmfH3zwge655x7FxcXJZrNpw4YNbtuNMcrOzlZcXJxCQ0M1YsQI7d+//7zHfeONN9S/f38FBwerf//+Wr9+fTuN4MK1Ntb6+nr94he/UHJyssLDwxUXF6fJkyfr+PHjrR6zsLCw2fN8+vTpdh5Ny853TqdOndqk3ptvvvm8x/W3c3q+cTZ3Xmw2m5577rkWj+mP5/NCPk/88X3a6cPOH//4R2VmZmrBggUqLS3VbbfdpjFjxujIkSPN9i8rK9Ndd92l2267TaWlpfrlL3+pn/3sZ3rjjTcuceUXbtu2bXr88ce1Y8cOFRUV6cyZM0pLS9OpU+f/0c4DBw6ovLzctSQmJl6Citvm+uuvd6t57969LfbtiOdTknbu3Ok2xrNfqnn//fe3up+/n89Tp05pwIABWr58ebPbFy1apLy8PC1fvlw7d+6U3W7XqFGjVFNT0+IxP/roIz3wwAN6+OGHtWfPHj388MOaMGGCPv744/YaxgVpbazfffedPvnkE/3Hf/yHPvnkE61bt04HDx7UuHHjznvcyMhIt3NcXl6ukJCQ9hjCBTnfOZWkO++8063et956q9Vj+uM5Pd84zz0nf/jDH2Sz2XTfffe1elx/O58X8nnil+9T08n9+Mc/No899phb27XXXmuefPLJZvvPmzfPXHvttW5tM2bMMDfffHO71ehtFRUVRpLZtm1bi322bNliJJnKyspLV5gXPPXUU2bAgAEX3N8K59MYY2bPnm369OljGhsbm93eEc+nJLN+/XrXemNjo7Hb7WbhwoWuttOnT5uoqCjzwgsvtHicCRMmmDvvvNOtbfTo0WbixIler9lT5461OX/961+NJHP48OEW+xQUFJioqCjvFudFzY1zypQpZvz48Rd1HH8/pxdyPsePH2/uuOOOVvv4+/k0punnib++Tzv1zE5dXZ1KSkqUlpbm1p6Wlqbt27c3u89HH33UpP/o0aO1a9cu1dfXt1ut3lRVVSVJ6tat23n73njjjYqNjVVqaqq2bNnS3qV5xaFDhxQXF6eEhARNnDhRX3zxRYt9rXA+6+rqtHr1ak2bNq3FH789qyOez7PKysrkcDjczldwcLCGDx/e4vtVavkct7aPP6qqqpLNZjvv7/idPHlSvXv3Vs+ePTV27FiVlpZemgLbYOvWrerRo4f69u2rn/70p6qoqGi1f0c/p1999ZXefPNNTZ8+/bx9/f18nvt54q/v004ddr7++ms1NDQ0+cHQmJiYJj8sepbD4Wi2/5kzZ/T111+3W63eYozRnDlzdOuttyopKanFfrGxsXrppZf0xhtvaN26derXr59SU1P1wQcfXMJqL97gwYP1yiuv6N1339XLL78sh8OhoUOH6sSJE8327+jnU5I2bNigb7/9VlOnTm2xT0c9nz909j15Me/Xs/td7D7+5vTp03ryySc1adKkVn9I8dprr1VhYaE2btyoV199VSEhIbrlllt06NChS1jtxRkzZoz++7//W++//74WL16snTt36o477pDT6Wxxn45+TleuXKmIiAhlZGS02s/fz2dznyf++j61xM9FtNW5/xs2xrT6P+Tm+jfX7o+eeOIJffrppyouLm61X79+/dSvXz/X+pAhQ3T06FH95je/0bBhw9q7TI+NGTPG9efk5GQNGTJEffr00cqVKzVnzpxm9+nI51OS8vPzNWbMGMXFxbXYp6Oez+Zc7PvV0338RX19vSZOnKjGxkY9//zzrfa9+eab3W7uveWWWzRw4EAtW7ZMv/vd79q7VI888MADrj8nJSVp0KBB6t27t958881Ww0BHPqd/+MMf9NBDD5333ht/P5+tfZ742/u0U8/sdO/eXV26dGmSHCsqKpokzLPsdnuz/QMCAhQdHd1utXrDrFmztHHjRm3ZskU9e/a86P1vvvlmv/kfxYUKDw9XcnJyi3V35PMpSYcPH9bmzZv16KOPXvS+He18nn2q7mLer2f3u9h9/EV9fb0mTJigsrIyFRUVtTqr05zLLrtMN910U4c6z7Gxserdu3erNXfkc/qXv/xFBw4c8Og960/ns6XPE399n3bqsBMUFKSUlBTXkyxnFRUVaejQoc3uM2TIkCb9N23apEGDBikwMLDdam0LY4yeeOIJrVu3Tu+//74SEhI8Ok5paaliY2O9XF37cjqd+uyzz1qsuyOezx8qKChQjx49dPfdd1/0vh3tfCYkJMhut7udr7q6Om3btq3F96vU8jlubR9/cDboHDp0SJs3b/YofBtjtHv37g51nk+cOKGjR4+2WnNHPafS9zOxKSkpGjBgwEXv6w/n83yfJ377PvXKbc4d2Nq1a01gYKDJz883f/vb30xmZqYJDw83//jHP4wxxjz55JPm4YcfdvX/4osvTFhYmPn5z39u/va3v5n8/HwTGBhoXn/9dV8N4bz+7d/+zURFRZmtW7ea8vJy1/Ldd9+5+pw7ziVLlpj169ebgwcPmn379pknn3zSSDJvvPGGL4ZwwbKysszWrVvNF198YXbs2GHGjh1rIiIiLHU+z2poaDC9evUyv/jFL5ps66jns6amxpSWlprS0lIjyeTl5ZnS0lLXE0gLFy40UVFRZt26dWbv3r3mwQcfNLGxsaa6utp1jIcfftjtacoPP/zQdOnSxSxcuNB89tlnZuHChSYgIMDs2LHjko/vh1oba319vRk3bpzp2bOn2b17t9v71ul0uo5x7lizs7PNO++8Yz7//HNTWlpqHnnkERMQEGA+/vhjXwzRGNP6OGtqakxWVpbZvn27KSsrM1u2bDFDhgwxP/rRjzrcOT3fv11jjKmqqjJhYWFmxYoVzR6jI5zPC/k88cf3aacPO8YY8/vf/9707t3bBAUFmYEDB7o9kj1lyhQzfPhwt/5bt241N954owkKCjJXXXVVi/9w/YWkZpeCggJXn3PH+eyzz5o+ffqYkJAQ07VrV3PrrbeaN99889IXf5EeeOABExsbawIDA01cXJzJyMgw+/fvd223wvk869133zWSzIEDB5ps66jn8+wj8ucuU6ZMMcZ8/1jrU089Zex2uwkODjbDhg0ze/fudTvG8OHDXf3Peu2110y/fv1MYGCgufbaa/0i5LU21rKyshbft1u2bHEd49yxZmZmml69epmgoCBz5ZVXmrS0NLN9+/ZLP7gfaG2c3333nUlLSzNXXnmlCQwMNL169TJTpkwxR44ccTtGRzin5/u3a4wxL774ogkNDTXffvtts8foCOfzQj5P/PF9avt/xQMAAFhSp75nBwAAWB9hBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAuwtSpU5Wenu7rMgBcBMIOAJ+oqKjQjBkz1KtXLwUHB8tut2v06NH66KOPfF0aAIsJ8HUBADqn++67T/X19Vq5cqWuvvpqffXVV3rvvff0zTff+Lo0ABbDzA6AS+7bb79VcXGxnn32Wd1+++3q3bu3fvzjH2v+/Pm6++67JUlVVVX613/9V/Xo0UORkZG64447tGfPHrfjbNy4UYMGDVJISIi6d++ujIwM17bKykpNnjxZXbt2VVhYmMaMGaNDhw65thcWFuqKK67Qu+++q+uuu06XX3657rzzTpWXl7v6NDQ0aM6cObriiisUHR2tefPm6dyfE3z99deVnJys0NBQRUdHa+TIkTp16lR7/LUB8BBhB8Ald/nll+vyyy/Xhg0b5HQ6m2w3xujuu++Ww+HQW2+9pZKSEg0cOFCpqamumZ8333xTGRkZuvvuu1VaWqr33ntPgwYNch1j6tSp2rVrlzZu3KiPPvpIxhjdddddqq+vd/X57rvv9Jvf/EarVq3SBx98oCNHjmju3Lmu7YsXL9Yf/vAH5efnq7i4WN98843Wr1/v2l5eXq4HH3xQ06ZN02effaatW7cqIyOjSSAC4GNe+/10ALgIr7/+uunatasJCQkxQ4cONfPnzzd79uwxxhjz3nvvmcjISHP69Gm3ffr06WNefPFFY4wxQ4YMMQ899FCzxz548KCRZD788ENX29dff21CQ0PNn/70J2OMMQUFBUaS+fvf/+7q8/vf/97ExMS41mNjY83ChQtd6/X19aZnz55m/PjxxhhjSkpKjCTzj3/8ow1/EwDaGzM7AHzivvvu0/Hjx7Vx40aNHj1aW7du1cCBA1VYWKiSkhKdPHlS0dHRrlmgyy+/XGVlZfr8888lSbt371Zqamqzx/7ss88UEBCgwYMHu9qio6PVr18/ffbZZ662sLAw9enTx7UeGxuriooKSd9fRisvL9eQIUNc2wMCAtxmjwYMGKDU1FQlJyfr/vvv18svv6zKykrv/AUB8BrCDgCfCQkJ0ahRo/R//s//0fbt2zV16lQ99dRTamxsVGxsrHbv3u22HDhwQP/+7/8uSQoNDW3xuKaFy0jGGNlsNtd6YGCg23abzXZRl6C6dOmioqIivf322+rfv7+WLVumfv36qays7IKPAaD9EXYA+I3+/fvr1KlTGjhwoBwOhwICAnTNNde4Ld27d5ck3XDDDXrvvfdaPM6ZM2f08ccfu9pOnDihgwcP6rrrrrugWqKiohQbG6sdO3a42s6cOaOSkhK3fjabTbfccouefvpplZaWKigoyO2+HgC+x6PnAC65EydO6P7779e0adN0ww03KCIiQrt27dKiRYs0fvx4jRw5UkOGDFF6erqeffZZ9evXT8ePH9dbb72l9PR0DRo0SE899ZRSU1PVp08fTZw4UWfOnNHbb7+tefPmKTExUePHj9dPf/pTvfjii4qIiNCTTz6pH/3oRxo/fvwF1zl79mwtXLhQiYmJuu6665SXl6dvv/3Wtf3jjz/We++9p7S0NPXo0UMff/yx/vnPf15woAJwaRB2AFxyl19+uQYPHqwlS5bo888/V319veLj4/XTn/5Uv/zlL2Wz2fTWW29pwYIFmjZtmv75z3/Kbrdr2LBhiomJkSSNGDFCr732mn79619r4cKFioyM1LBhw1yvUVBQoNmzZ2vs2LGqq6vTsGHD9NZbbzW5dNWarKwslZeXa+rUqbrssss0bdo03XvvvaqqqpIkRUZG6oMPPtDSpUtVXV2t3r17a/HixRozZox3/8IAtInNXMwFagAAgA6Ge3YAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAICl/V8sfieI3iN8AAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Seconds')\n",
    "plt.ylabel('Frequency')\n",
    "plt.hist(np.array(lenList), bins='auto', color='#0504aa',\n",
    "                            alpha=0.7, rwidth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "312bb183",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trimming the audio files\n",
    "recListTrimmed = []\n",
    "lenListTrimmed = []\n",
    "\n",
    "for i in df[\"path\"]:    \n",
    "    rec, sr = librosa.load(i)\n",
    "    rec, index = librosa.effects.trim(rec, top_db=30)\n",
    "    \n",
    "    twoSec = sr * 3\n",
    "    \n",
    "    if len(rec) > twoSec:\n",
    "        rec = rec[:twoSec]\n",
    "\n",
    "    recListTrimmed.append([rec, sr])\n",
    "    lenListTrimmed.append(len(rec)/sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3a72cdf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.,   9.,  14.,  27.,  44.,  64.,  88., 145., 181., 188., 244.,\n",
       "        314., 379., 442., 496., 563., 547., 586., 550., 490., 414., 444.,\n",
       "        393., 341., 296., 268., 242., 211., 208., 181., 146., 131., 130.,\n",
       "        125., 100.,  88.,  74.,  86.,  59.,  67.,  44.,  48.,  47.,  24.,\n",
       "         19.,  29.,  29.,  24.,  25.,  14.,  25.,  15.,   6.,  13.,   7.,\n",
       "         12.,  10.,   4.,   7.,  74.]),\n",
       " array([0.18575964, 0.23266364, 0.27956765, 0.32647166, 0.37337566,\n",
       "        0.42027967, 0.46718367, 0.51408768, 0.56099169, 0.60789569,\n",
       "        0.6547997 , 0.7017037 , 0.74860771, 0.79551172, 0.84241572,\n",
       "        0.88931973, 0.93622373, 0.98312774, 1.03003175, 1.07693575,\n",
       "        1.12383976, 1.17074376, 1.21764777, 1.26455178, 1.31145578,\n",
       "        1.35835979, 1.40526379, 1.4521678 , 1.49907181, 1.54597581,\n",
       "        1.59287982, 1.63978382, 1.68668783, 1.73359184, 1.78049584,\n",
       "        1.82739985, 1.87430385, 1.92120786, 1.96811187, 2.01501587,\n",
       "        2.06191988, 2.10882389, 2.15572789, 2.2026319 , 2.2495359 ,\n",
       "        2.29643991, 2.34334392, 2.39024792, 2.43715193, 2.48405593,\n",
       "        2.53095994, 2.57786395, 2.62476795, 2.67167196, 2.71857596,\n",
       "        2.76547997, 2.81238398, 2.85928798, 2.90619199, 2.95309599,\n",
       "        3.        ]),\n",
       " <BarContainer object of 60 artists>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ploting the duration of the trimmed files\n",
    "plt.xlabel('Seconds')\n",
    "plt.ylabel('Frequency')\n",
    "plt.hist(np.array(lenListTrimmed), bins='auto', color='#0504aa',\n",
    "                            alpha=0.7, rwidth=1) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "06a2e133",
   "metadata": {},
   "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>Id</th>\n",
       "      <th>path</th>\n",
       "      <th>speakerId</th>\n",
       "      <th>action</th>\n",
       "      <th>object</th>\n",
       "      <th>Self-reported fluency level</th>\n",
       "      <th>First Language spoken</th>\n",
       "      <th>Current language used for work/school</th>\n",
       "      <th>gender</th>\n",
       "      <th>ageRange</th>\n",
       "      <th>SampleRates</th>\n",
       "      <th>AudioData</th>\n",
       "      <th>recordings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0a312...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>change language</td>\n",
       "      <td>none</td>\n",
       "      <td>advanced</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>female</td>\n",
       "      <td>22-40</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[[-8.641122e-06, 1.5207445e-06, 2.6109909e-05,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0ee42...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>activate</td>\n",
       "      <td>music</td>\n",
       "      <td>advanced</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>female</td>\n",
       "      <td>22-40</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[[-7.505116e-05, -7.6106694e-06, 1.10456285e-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/1d9f3...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>deactivate</td>\n",
       "      <td>lights</td>\n",
       "      <td>advanced</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>female</td>\n",
       "      <td>22-40</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[[3.145638e-05, 3.0807154e-05, 2.8492566e-05, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/269fc...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>increase</td>\n",
       "      <td>volume</td>\n",
       "      <td>advanced</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>female</td>\n",
       "      <td>22-40</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[[0.0005632056, 0.0005720459, 0.00071935874, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/5bbda...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>increase</td>\n",
       "      <td>volume</td>\n",
       "      <td>advanced</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>English (United States)</td>\n",
       "      <td>female</td>\n",
       "      <td>22-40</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[[4.5547e-06, 2.4867417e-05, 3.363467e-05, 3.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id                                               path         speakerId  \\\n",
       "0   0  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0a312...  2BqVo8kVB2Skwgyb   \n",
       "1   1  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0ee42...  2BqVo8kVB2Skwgyb   \n",
       "2   2  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/1d9f3...  2BqVo8kVB2Skwgyb   \n",
       "3   3  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/269fc...  2BqVo8kVB2Skwgyb   \n",
       "4   4  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/5bbda...  2BqVo8kVB2Skwgyb   \n",
       "\n",
       "            action  object Self-reported fluency level   \\\n",
       "0  change language    none                     advanced   \n",
       "1         activate   music                     advanced   \n",
       "2       deactivate  lights                     advanced   \n",
       "3         increase  volume                     advanced   \n",
       "4         increase  volume                     advanced   \n",
       "\n",
       "     First Language spoken Current language used for work/school  gender  \\\n",
       "0  English (United States)               English (United States)  female   \n",
       "1  English (United States)               English (United States)  female   \n",
       "2  English (United States)               English (United States)  female   \n",
       "3  English (United States)               English (United States)  female   \n",
       "4  English (United States)               English (United States)  female   \n",
       "\n",
       "  ageRange  SampleRates                                          AudioData  \\\n",
       "0    22-40        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "1    22-40        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "2    22-40        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "3    22-40        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "4    22-40        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "\n",
       "                                          recordings  \n",
       "0  [[-8.641122e-06, 1.5207445e-06, 2.6109909e-05,...  \n",
       "1  [[-7.505116e-05, -7.6106694e-06, 1.10456285e-0...  \n",
       "2  [[3.145638e-05, 3.0807154e-05, 2.8492566e-05, ...  \n",
       "3  [[0.0005632056, 0.0005720459, 0.00071935874, 0...  \n",
       "4  [[4.5547e-06, 2.4867417e-05, 3.363467e-05, 3.0...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfNew = df\n",
    "dfNew[\"recordings\"] = recListTrimmed\n",
    "\n",
    "dfNew.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "a6d97ceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_spectrogram(audio, sr, n_blocks):\n",
    "    frequencies, times, spec = spectrogram(audio, fs=sr)\n",
    "    times = (times * 1000).astype(int) #milliseconds\n",
    "    frequencies = frequencies.astype(int)\n",
    "    fig, ax = plt.subplots(1,1)\n",
    "    plt.pcolormesh(times, frequencies, 10*np.log10(spec)) #changed the scale to dB\n",
    "    plt.colorbar()\n",
    "    time_bounds = list()\n",
    "    freq_bounds = list()\n",
    "    #n_blocks is the same vertically and horizontally\n",
    "    #starting from vertical lines to create time bins:\n",
    "    v_step = int(times[len(times)-1] / n_blocks)\n",
    "    \n",
    "    for v in range(0, times[len(times)-1], v_step):\n",
    "        ax.axvline(v, color = 'red')\n",
    "        time_bounds.append(v)\n",
    "    #and then horizontal lines to create frequency bins:\n",
    "    h_step = int(frequencies[len(frequencies)-1]/n_blocks)\n",
    "    for h in range(0, frequencies[len(frequencies)-1], h_step):\n",
    "        ax.axhline(h, color = 'red')\n",
    "        freq_bounds.append(h)\n",
    "        \n",
    "    \n",
    "    # Split the matrix into sub-arrays of size N along the first axis (rows)\n",
    "    rows_blocks = np.array_split(spec, n_blocks, axis=0)\n",
    "    #this is a list: each element of the list is a sub-matrix obtained slicing horizontally the spectrogram\n",
    "    # into n_blocks slices\n",
    "\n",
    "    # Split the sub_matrices into sub-arrays of size NxN along the second axis (columns)\n",
    "    blocks = [ np.array_split(block, n_blocks, axis=1) for block in rows_blocks]\n",
    "    #This is, instead, a list of lists: the first element is a list containing all the blocks obtained from \n",
    "    #the first vertical slice\n",
    "\n",
    "\n",
    "    # Initialize arrays to store the mean and standard deviation of each block\n",
    "    block_means = np.zeros((n_blocks,n_blocks))\n",
    "    block_stds = np.zeros((n_blocks,n_blocks))\n",
    "\n",
    "    # Iterate over blocks\n",
    "    for i in range(0, n_blocks):\n",
    "        for j in range(0, n_blocks):\n",
    "            # Compute mean and standard deviation of the block\n",
    "             #i is the index of the horizontal split  (indicating which column)\n",
    "            block = blocks[i][j] #j is the index of the vertical split (indicating which row)\n",
    "            mean = np.mean(block)\n",
    "            std = np.std(block)\n",
    "            block_means[i,j] = mean\n",
    "            block_stds[i,j] = std\n",
    "    plt.xlabel(\"Time (ms)\")\n",
    "    plt.xlim(left=0)\n",
    "    plt.ylabel(\"Frequency (Hz)\")\n",
    "    plt.show()\n",
    "    return block_means, block_stds, blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "97e98a2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " avg, std_dev, blocks = split_spectrogram(df['AudioData'].loc[0], df['SampleRates'].loc[0], 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "c44bea81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.02114151e-03, 4.47144684e+02, 5.34698303e+02, 3.53419006e+02,\n",
       "        2.65503815e+02, 3.54775963e+01, 2.79979897e-03],\n",
       "       [9.61953425e-04, 2.64356017e+00, 1.26033580e+00, 2.36499161e+02,\n",
       "        4.46640921e+00, 1.35743916e-01, 1.11565981e-02],\n",
       "       [1.69032573e-04, 2.08977692e+02, 5.61042404e+01, 2.17512989e+01,\n",
       "        2.32271194e+01, 1.02875412e+00, 2.82459594e-02],\n",
       "       [9.08299917e-05, 1.93983040e+01, 1.42254601e+01, 4.89650917e+00,\n",
       "        1.78332853e+00, 4.74329615e+00, 2.11712569e-01],\n",
       "       [9.14723278e-05, 1.19358492e+01, 1.34936924e+01, 7.37270951e-01,\n",
       "        1.84629634e-01, 3.05265737e+00, 7.43939504e-02],\n",
       "       [7.53234781e-05, 3.19165826e+00, 7.63929367e+00, 2.50292681e-02,\n",
       "        5.78181520e-02, 8.81312966e-01, 2.28207037e-02],\n",
       "       [5.06264114e-05, 1.42704785e+00, 2.34564447e+00, 1.30259693e-01,\n",
       "        9.34351534e-02, 4.76612836e-01, 5.68679906e-03]])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "4ed46055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.82100460e-02, 1.50794958e+03, 1.82468530e+03, 7.27139709e+02,\n",
       "        5.16024841e+02, 2.06723099e+02, 5.73942810e-03],\n",
       "       [2.88074790e-03, 7.75138521e+00, 2.82774878e+00, 5.81195496e+02,\n",
       "        1.47538862e+01, 5.69272518e-01, 5.84353507e-02],\n",
       "       [3.37001577e-04, 6.93912537e+02, 2.65199829e+02, 4.99582558e+01,\n",
       "        7.64947662e+01, 2.62316346e+00, 9.65597332e-02],\n",
       "       [1.60906100e-04, 5.41228790e+01, 4.23374100e+01, 9.92438030e+00,\n",
       "        3.92146587e+00, 1.24899530e+01, 7.23723412e-01],\n",
       "       [1.61714182e-04, 3.46320381e+01, 3.77558441e+01, 2.13116789e+00,\n",
       "        5.46895981e-01, 6.42642260e+00, 2.04608887e-01],\n",
       "       [1.03867649e-04, 1.06427898e+01, 2.21253414e+01, 8.65658745e-02,\n",
       "        1.15742072e-01, 1.47735786e+00, 6.61128908e-02],\n",
       "       [5.93416080e-05, 4.03089190e+00, 7.66315031e+00, 2.37771779e-01,\n",
       "        2.30977476e-01, 7.99147129e-01, 2.22179536e-02]])"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "04332517",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3  4]\n",
      " [ 5  6  7  8  9]\n",
      " [10 11 12 13 14]\n",
      " [15 16 17 18 19]]\n",
      "[array([[0, 1, 2, 3, 4],\n",
      "       [5, 6, 7, 8, 9]]), array([[10, 11, 12, 13, 14],\n",
      "       [15, 16, 17, 18, 19]])]\n",
      "[[array([[0],\n",
      "       [5]]), array([[1],\n",
      "       [6]]), array([[2],\n",
      "       [7]]), array([[3],\n",
      "       [8]]), array([[4],\n",
      "       [9]])], [array([[10],\n",
      "       [15]]), array([[11],\n",
      "       [16]]), array([[12],\n",
      "       [17]]), array([[13],\n",
      "       [18]]), array([[14],\n",
      "       [19]])]]\n",
      "[[ 2.5  3.5  4.5  5.5  6.5]\n",
      " [12.5 13.5 14.5 15.5 16.5]]\n"
     ]
    }
   ],
   "source": [
    "matr = np.array(([0,1,2,3,4],[5,6,7,8,9],[10,11,12,13,14], [15,16,17,18,19]))\n",
    "print(matr)\n",
    "rows_blocks = np.array_split(matr,2, axis=0)\n",
    "print(rows_blocks)\n",
    "blocks = [ np.array_split(block, 5, axis=1) for block in rows_blocks]\n",
    "print(blocks)\n",
    "block_means = np.zeros((2,5))\n",
    "block_stds = np.zeros((2,5))\n",
    "\n",
    "for i in range(0, 2):\n",
    "        for j in range(0, 5):\n",
    "            # Compute mean and standard deviation of the block\n",
    "             #i is the index of the horizontal split  (indicating which column)\n",
    "            block = blocks[i][j] #j is the index of the vertical split (indicating which row)\n",
    "            mean = np.mean(block)\n",
    "            std = np.std(block)\n",
    "            block_means[i,j] = mean\n",
    "            block_stds[i,j] = std\n",
    "print(block_means)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a310235e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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