preprocessing.ipynb 100 KB
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/user/opt/anaconda3/envs/capstone/lib/python3.7/site-packages/librosa/util/decorators.py:9: NumbaDeprecationWarning: An import was requested from a module that has moved location.\n",
      "Import requested from: 'numba.decorators', please update to use 'numba.core.decorators' or pin to Numba version 0.48.0. This alias will not be present in Numba version 0.50.0.\n",
      "  from numba.decorators import jit as optional_jit\n",
      "/Users/user/opt/anaconda3/envs/capstone/lib/python3.7/site-packages/librosa/util/decorators.py:9: NumbaDeprecationWarning: An import was requested from a module that has moved location.\n",
      "Import of 'jit' requested from: 'numba.decorators', please update to use 'numba.core.decorators' or pin to Numba version 0.48.0. This alias will not be present in Numba version 0.50.0.\n",
      "  from numba.decorators import jit as optional_jit\n"
     ]
    }
   ],
   "source": [
    "from utils import *\n",
    "\n",
    "from scipy import signal\n",
    "import matplotlib.pyplot as plt\n",
    "import librosa\n",
    "import python_speech_features\n",
    "import math\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from pysndfx import AudioEffectsChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT_DIR = get_upper_dir()\n",
    "DATA_DIR = get_data_noise_dir(PROJECT_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_list = extract_file_name(DATA_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def band_butter_pass(lowcut, highcut, fs, order=5):\n",
    "    nyq = 0.5 * fs\n",
    "    low = lowcut/nyq\n",
    "    high = highcut / nyq\n",
    "    b, a = signal.butter(order, [low, high], btype='band')\n",
    "    return b, a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def band_butter_filter(data, lowcut, highcut, fs):\n",
    "    b, a = band_butter_pass(lowcut, highcut, fs)\n",
    "    y = signal.lfilter(b, a, data)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_noise_centroid_mb(y, sr):\n",
    "    cent = librosa.feature.spectral_centroid(y=y, sr=sr)\n",
    "\n",
    "    threshold_h = np.max(cent)\n",
    "    threshold_l = np.min(cent)\n",
    "\n",
    "    less_noise = AudioEffectsChain().lowshelf(gain=-12.0, frequency=threshold_l, slope=0.5).highshelf(gain=-12.0, frequency=threshold_h, slope=0.5).limiter(gain=6.0)\n",
    "\n",
    "    y_cleaned = less_noise(y)\n",
    "\n",
    "    return y_cleaned\n",
    "\n",
    "\n",
    "    #return y_clean_boosted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_noise_mfcc_up(y, sr):\n",
    "\n",
    "    hop_length = 512\n",
    "\n",
    "    mfcc = python_speech_features.base.mfcc(y)\n",
    "    mfcc = python_speech_features.base.logfbank(y)\n",
    "    mfcc = python_speech_features.base.lifter(mfcc)\n",
    "\n",
    "    sum_of_squares = []\n",
    "    index = -1\n",
    "    for r in mfcc:\n",
    "        sum_of_squares.append(0)\n",
    "        index = index + 1\n",
    "        for n in r:\n",
    "            sum_of_squares[index] = sum_of_squares[index] + n**2\n",
    "\n",
    "    strongest_frame = sum_of_squares.index(max(sum_of_squares))\n",
    "    hz = python_speech_features.base.mel2hz(mfcc[strongest_frame])\n",
    "\n",
    "    max_hz = max(hz)\n",
    "    min_hz = min(hz)\n",
    "\n",
    "    speech_booster = AudioEffectsChain().lowshelf(frequency=min_hz*(-1), gain=12.0, slope=0.5)#.highshelf(frequency=min_hz*(-1)*1.2, gain=-12.0, slope=0.5)#.limiter(gain=8.0)\n",
    "    y_speach_boosted = speech_booster(y)\n",
    "\n",
    "    return (y_speach_boosted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_noise_power(y, sr):\n",
    "\n",
    "    cent = librosa.feature.spectral_centroid(y=y, sr=sr)\n",
    "\n",
    "    threshold_h = round(np.median(cent))*1.5\n",
    "    threshold_l = round(np.median(cent))*0.1\n",
    "\n",
    "    less_noise = AudioEffectsChain().lowshelf(gain=-30.0, frequency=threshold_l, slope=0.8).highshelf(gain=-12.0, frequency=threshold_h, slope=0.5)#.limiter(gain=6.0)\n",
    "    y_clean = less_noise(y)\n",
    "\n",
    "    return y_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def noise_reduction(x, sr, lowpass, highpass):\n",
    "    y = band_butter_filter(x, lowpass, highpass, sr)\n",
    "    y_mfcc_up = reduce_noise_mfcc_up(y, sr)\n",
    "    y_centroid = reduce_noise_centroid_mb(y_mfcc_up, sr)\n",
    "    y_cleaned = reduce_noise_power(y, sr)\n",
    "    \n",
    "    return y_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1800/1800 [08:28<00:00,  3.54it/s]\n"
     ]
    }
   ],
   "source": [
    "lowpass = 1700\n",
    "highpass = 2300\n",
    "\n",
    "for name in tqdm(name_list):\n",
    "    ch1_name = DATA_DIR + '/' + name + '_ch1.wav'\n",
    "    ch2_name = DATA_DIR + '/' + name + '_ch2.wav'\n",
    "    x1, fs1 = librosa.load(ch1_name)\n",
    "    x2, fs2 = librosa.load(ch2_name)\n",
    "    \n",
    "    y1 = noise_reduction(x1, fs1, lowpass, highpass)\n",
    "    save_wav(y1, fs1, name, '_ch1.wav', PROJECT_DIR)\n",
    "\n",
    "    y2 = noise_reduction(x2, fs2, lowpass, highpass)\n",
    "    save_wav(y2, fs2, name, '_ch2.wav', PROJECT_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa.display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "lowpass = 1700\n",
    "highpass = 2300\n",
    "x1, fs1 = librosa.load(DATA_DIR + '/' + name_list[140] + '_ch1.wav')\n",
    "y1 = noise_reduction(x1, fs1, lowpass, highpass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x1a1dabaf10>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "librosa.display.waveplot(x1, sr=fs1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x1a1da17450>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "librosa.display.waveplot(y1, sr=fs1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "x2, fs2 = librosa.load(DATA_DIR + '/' + name_list[190] + '_ch1.wav')\n",
    "y2 = noise_reduction(x2, fs2, lowpass, highpass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x1a1f136890>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "librosa.display.waveplot(x1, sr=fs1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data_709_46_angle_040'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_list[190]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "librosa.display.waveplot(z1, sr=fs1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2400"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(name_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.7"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}