heartbeat-segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "Author: Ani Aggarwal  \n",
    "Github: www.github.com/AniAggarwal"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "numerical-dubai",
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "import librosa.display\n",
    "\n",
    "from scipy.signal import butter, lfilter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pathlib import Path\n",
    "import csv\n",
    "import json\n",
    "\n",
    "import IPython.display as ipd\n",
    "\n",
    "from create_dataset import denoise_audio_np, process_audio_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "domestic-alarm",
   "metadata": {},
   "outputs": [],
   "source": [
    "DOWN_SR = 1000\n",
    "BPF_LOW = 20\n",
    "BPF_HIGH = 400\n",
    "BPF_ORDER = 3\n",
    "DATA_SR = 44100\n",
    "\n",
    "AUDIO_LEN = 1  # in seconds\n",
    "num_samples = AUDIO_LEN * DOWN_SR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "further-subsection",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = Path(\"./datasets/segmentation/heartbeat-sounds-kaggle/set_a/\")\n",
    "audio_path = train_path / \"normal__201102081321.wav\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "casual-booking",
   "metadata": {},
   "outputs": [],
   "source": [
    "audio, _ = librosa.load(audio_path, sr=DOWN_SR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "palestinian-halloween",
   "metadata": {},
   "outputs": [],
   "source": [
    "audio = denoise_audio_np(audio, bpf_high=195)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "opening-youth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2880x720 with 0 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<librosa.display.AdaptiveWaveplot at 0x2cf89b9d280>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40, 10))\n",
    "librosa.display.waveshow(audio[200:2000], sr=DOWN_SR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "combined-hudson",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\miniconda3\\envs\\flow\\lib\\site-packages\\librosa\\filters.py:238: UserWarning: Empty filters detected in mel frequency basis. Some channels will produce empty responses. Try increasing your sampling rate (and fmax) or reducing n_mels.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "n_fft = int(0.025 * DOWN_SR)\n",
    "hop_len = int(0.01 * DOWN_SR)\n",
    "    \n",
    "mfcc = librosa.feature.mfcc(\n",
    "    audio, DOWN_SR, n_fft=n_fft, hop_length=hop_len, n_mfcc=13\n",
    ")\n",
    "mfcc = mfcc.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "checked-rochester",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(789, 13)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mfcc.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "resident-lightning",
   "metadata": {},
   "source": [
    "# MFCC Shape\n",
    "len = len(audio) / hop_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "previous-athletics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2cf8c244220>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2cf8c274310>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MFCC')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "librosa.display.specshow(mfcc, sr=DOWN_SR, hop_length=hop_len)\n",
    "\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"MFCC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interracial-fellow",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "polish-comparative",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dir = Path(\"./datasets/segmentation/heartbeat-sounds-kaggle/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ranging-amount",
   "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>fname</th>\n",
       "      <th>cycle</th>\n",
       "      <th>sound</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>set_a/normal__201102081321.wav</td>\n",
       "      <td>1</td>\n",
       "      <td>S1</td>\n",
       "      <td>10021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>set_a/normal__201102081321.wav</td>\n",
       "      <td>1</td>\n",
       "      <td>S2</td>\n",
       "      <td>20759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>set_a/normal__201102081321.wav</td>\n",
       "      <td>2</td>\n",
       "      <td>S1</td>\n",
       "      <td>35075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>set_a/normal__201102081321.wav</td>\n",
       "      <td>2</td>\n",
       "      <td>S2</td>\n",
       "      <td>47244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>set_a/normal__201102081321.wav</td>\n",
       "      <td>3</td>\n",
       "      <td>S1</td>\n",
       "      <td>62992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>set_a/normal__201108011118.wav</td>\n",
       "      <td>10</td>\n",
       "      <td>S2</td>\n",
       "      <td>284673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>set_a/normal__201108011118.wav</td>\n",
       "      <td>11</td>\n",
       "      <td>S1</td>\n",
       "      <td>300863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>set_a/normal__201108011118.wav</td>\n",
       "      <td>11</td>\n",
       "      <td>S2</td>\n",
       "      <td>314279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>set_a/normal__201108011118.wav</td>\n",
       "      <td>12</td>\n",
       "      <td>S1</td>\n",
       "      <td>330980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>set_a/normal__201108011118.wav</td>\n",
       "      <td>12</td>\n",
       "      <td>S2</td>\n",
       "      <td>343126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>390 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              fname  cycle sound  location\n",
       "0    set_a/normal__201102081321.wav      1    S1     10021\n",
       "1    set_a/normal__201102081321.wav      1    S2     20759\n",
       "2    set_a/normal__201102081321.wav      2    S1     35075\n",
       "3    set_a/normal__201102081321.wav      2    S2     47244\n",
       "4    set_a/normal__201102081321.wav      3    S1     62992\n",
       "..                              ...    ...   ...       ...\n",
       "385  set_a/normal__201108011118.wav     10    S2    284673\n",
       "386  set_a/normal__201108011118.wav     11    S1    300863\n",
       "387  set_a/normal__201108011118.wav     11    S2    314279\n",
       "388  set_a/normal__201108011118.wav     12    S1    330980\n",
       "389  set_a/normal__201108011118.wav     12    S2    343126\n",
       "\n",
       "[390 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(dataset_dir / \"set_a_timing.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "anticipated-vault",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing file set_a/normal__201102081321.wav\n",
      "processing file set_a/normal__201102260502.wav\n",
      "processing file set_a/normal__201103090635.wav\n",
      "processing file set_a/normal__201103140132.wav\n",
      "processing file set_a/normal__201103140822.wav\n",
      "processing file set_a/normal__201103151912.wav\n",
      "processing file set_a/normal__201103221214.wav\n",
      "processing file set_a/normal__201104141251.wav\n",
      "processing file set_a/normal__201105011626.wav\n",
      "processing file set_a/normal__201105021654.wav\n",
      "processing file set_a/normal__201105021804.wav\n",
      "processing file set_a/normal__201105151450.wav\n",
      "processing file set_a/normal__201106111136.wav\n",
      "processing file set_a/normal__201106141148.wav\n",
      "processing file set_a/normal__201106210943.wav\n",
      "processing file set_a/normal__201106221418.wav\n",
      "processing file set_a/normal__201106221450.wav\n",
      "processing file set_a/normal__201108011112.wav\n",
      "processing file set_a/normal__201108011114.wav\n",
      "processing file set_a/normal__201108011115.wav\n",
      "processing file set_a/normal__201108011118.wav\n"
     ]
    }
   ],
   "source": [
    "n_fft = int(0.025 * DOWN_SR)\n",
    "hop_len = int(0.01 * DOWN_SR)\n",
    "\n",
    "data = {\"mfccs\": [], \"labels\": []}\n",
    "class_to_int = {\"S1\": 0, \"S2\": 1}\n",
    "\n",
    "for filename, group in df.groupby([\"fname\"]):\n",
    "    print(f\"processing file {filename}\")\n",
    "    file_path = dataset_dir / filename\n",
    "    mfccs = process_audio_file(file_path, AUDIO_LEN)\n",
    "\n",
    "    labels = [None] * len(mfccs) * len(mfccs[0])\n",
    "    \n",
    "    prev_class_idx = None\n",
    "    for row_tuple in group[[\"sound\", \"location\"]].itertuples():\n",
    "        new_loc = int((int(row_tuple[2]) * DOWN_SR / DATA_SR) / hop_len)\n",
    "        if new_loc < len(labels) - 1:\n",
    "            if prev_class_idx is not None:\n",
    "                labels[prev_class_idx + 1: new_loc + 1] = [class_to_int[row_tuple[1]]] * (new_loc - prev_class_idx)\n",
    "            else:\n",
    "                labels[0: new_loc + 1] = [class_to_int[row_tuple[1]]] * (new_loc + 1)\n",
    "            \n",
    "            prev_class_idx = new_loc\n",
    "            \n",
    "    if labels[-1] is None:\n",
    "        labels[prev_class_idx + 1:] = [int(not labels[prev_class_idx])] * (len(labels) - prev_class_idx - 1)\n",
    "    \n",
    "    split_labels = []\n",
    "    for i in range(len(mfccs)):\n",
    "        split_labels.append(labels[i * len(mfccs[0]): (i + 1) * len(mfccs[0])])\n",
    "    \n",
    "    \n",
    "    data[\"mfccs\"] += mfccs\n",
    "    data[\"labels\"] += split_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "surprising-destination",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(output_path, \"w\") as f:\n",
    "    json.dump(data, f, indent=4)"
   ]
  }
 ],
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