feature_engineering.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "feature_engineering.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gaurav1610/Audio-Classification-Using-CNN/blob/master/feature_engineering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5lo2rXxol3v",
        "colab_type": "code",
        "outputId": "16d520c2-5901-4b1b-b60d-2ce2da1ccfbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "source": [
        "!pip install python_speech_features"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting python_speech_features\n",
            "  Downloading https://files.pythonhosted.org/packages/ff/d1/94c59e20a2631985fbd2124c45177abaa9e0a4eee8ba8a305aa26fc02a8e/python_speech_features-0.6.tar.gz\n",
            "Building wheels for collected packages: python-speech-features\n",
            "  Building wheel for python-speech-features (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-speech-features: filename=python_speech_features-0.6-cp36-none-any.whl size=5889 sha256=271d56ddb685890b53f694fce0bc49fbdacf1dd8bdf0de589db3b51b38235237\n",
            "  Stored in directory: /root/.cache/pip/wheels/3c/42/7c/f60e9d1b40015cd69b213ad90f7c18a9264cd745b9888134be\n",
            "Successfully built python-speech-features\n",
            "Installing collected packages: python-speech-features\n",
            "Successfully installed python-speech-features-0.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qF4M8UouGjo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "76990232-1698-4d94-ce2b-e43dcb3a04e3"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wWgcCKvWUiUS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "da230222-fc06-41d7-f457-bf0d6b6761de"
      },
      "source": [
        "cd drive/My Drive/Audio-Classification-master"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Audio-Classification-master\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLZN4WV3Vnlh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "from tqdm import tqdm\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.io import wavfile\n",
        "from python_speech_features import mfcc, logfbank\n",
        "import librosa"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eG19Z3pytAnJ",
        "colab_type": "code",
        "outputId": "c3f56512-2644-44b1-af64-4b5ce282b680",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        }
      },
      "source": [
        "def plot_signals(signals):\n",
        "    fig, axes = plt.subplots(nrows=2, ncols=5, sharex=False,\n",
        "                             sharey=True, figsize=(20,5))\n",
        "    fig.suptitle('Time Series', size=16)\n",
        "    i = 0\n",
        "    for x in range(2):\n",
        "        for y in range(5):\n",
        "            axes[x,y].set_title(list(signals.keys())[i])\n",
        "            axes[x,y].plot(list(signals.values())[i])\n",
        "            axes[x,y].get_xaxis().set_visible(False)\n",
        "            axes[x,y].get_yaxis().set_visible(False)\n",
        "            i += 1\n",
        "\n",
        "def plot_fft(fft):\n",
        "    fig, axes = plt.subplots(nrows=2, ncols=5, sharex=False,\n",
        "                             sharey=True, figsize=(20,5))\n",
        "    fig.suptitle('Fourier Transforms', size=16)\n",
        "    i = 0\n",
        "    for x in range(2):\n",
        "        for y in range(5):\n",
        "            data = list(fft.values())[i]\n",
        "            Y, freq = data[0], data[1]\n",
        "            axes[x,y].set_title(list(fft.keys())[i])\n",
        "            axes[x,y].plot(freq, Y)\n",
        "            axes[x,y].get_xaxis().set_visible(False)\n",
        "            axes[x,y].get_yaxis().set_visible(False)\n",
        "            i += 1\n",
        "\n",
        "def plot_fbank(fbank):\n",
        "    fig, axes = plt.subplots(nrows=2, ncols=5, sharex=False,\n",
        "                             sharey=True, figsize=(20,5))\n",
        "    fig.suptitle('Filter Bank Coefficients', size=16)\n",
        "    i = 0\n",
        "    for x in range(2):\n",
        "        for y in range(5):\n",
        "            axes[x,y].set_title(list(fbank.keys())[i])\n",
        "            axes[x,y].imshow(list(fbank.values())[i],\n",
        "                    cmap='hot', interpolation='nearest')\n",
        "            axes[x,y].get_xaxis().set_visible(False)\n",
        "            axes[x,y].get_yaxis().set_visible(False)\n",
        "            i += 1\n",
        "\n",
        "def plot_mfccs(mfccs):\n",
        "    fig, axes = plt.subplots(nrows=2, ncols=5, sharex=False,\n",
        "                             sharey=True, figsize=(20,5))\n",
        "    fig.suptitle('Mel Frequency Cepstrum Coefficients', size=16)\n",
        "    i = 0\n",
        "    for x in range(2):\n",
        "        for y in range(5):\n",
        "            axes[x,y].set_title(list(mfccs.keys())[i])\n",
        "            axes[x,y].imshow(list(mfccs.values())[i],\n",
        "                    cmap='hot', interpolation='nearest')\n",
        "            axes[x,y].get_xaxis().set_visible(False)\n",
        "            axes[x,y].get_yaxis().set_visible(False)\n",
        "            i += 1\n",
        "def envelope(y,rate,threshold):\n",
        "    mask =[]\n",
        "    y=pd.Series(y).apply(np.abs)\n",
        "    y_mean=y.rolling(window=int(rate/10),min_periods=1,center=True).mean\n",
        "    for m in y_mean:\n",
        "        if m > threshold:\n",
        "            mask.append(True)\n",
        "        else:\n",
        "            mask.append(False)\n",
        "    return mask\n",
        "\n",
        "\n",
        "def calc_fft(y,rate):\n",
        "    n=len(y)\n",
        "    freq=np.fft.rfftfreq(n,d=1/rate)\n",
        "    Y=abs(np.fft.rfft(y)/n)\n",
        "    return(Y,freq)\n",
        "\n",
        "df = pd.read_csv('instruments.csv')\n",
        "df.set_index('fname',inplace=True)\n",
        "\n",
        "\n",
        "\n",
        "for i in df.index:\n",
        "    rate,signal = wavfile.read('wavfiles/'+i)\n",
        "    df.at[i,'length'] = signal.shape[0]/rate\n",
        "\n",
        "classes=list(np.unique(df.label))\n",
        "class_dist = df.groupby(['label'])['length'].mean()\n",
        "fig,ax=plt.subplots()\n",
        "ax.set_title('Class Distribution',y=1.08)\n",
        "ax.pie(class_dist,labels=class_dist.index,autopct='%1.1f%%',shadow=False,startangle=90)\n",
        "ax.axis('equal')\n",
        "plt.show()\n",
        "df.reset_index(inplace=True)\n",
        "signals={}\n",
        "fft={}\n",
        "fbank={}\n",
        "mfccs={}\n",
        "\n",
        "for c in classes:\n",
        "    wav_file=df[df.label ==c].iloc[0,0]\n",
        "    signal,rate=librosa.load('wavfiles/'+wav_file,sr=44100)\n",
        "    mask = envelope(signal,rate,0.0005)\n",
        "    signal=signal[mask]\n",
        "    signals[c] = signal\n",
        "    fft[c]=calc_fft(signal,rate)\n",
        "\n",
        "\n",
        "    bank=logfbank(signal[:rate],rate,nfilt=16,nfft=1103).T\n",
        "    fbank[c] =bank\n",
        "    mel =mfcc(signal[:rate],rate,numcep = 13,nfilt=26,nfft=1103)\n",
        "    mfccs[c]=mel\n",
        "plot_signals(signals)\n",
        "plt.show()\n",
        "plot_fft(fft)\n",
        "plt.show()\n",
        "plot_fbank(fbank)\n",
        "plt.show()\n",
        "plot_mfccs(mfccs)\n",
        "plt.show()\n",
        "\n",
        "if len(os.listdir('clean')) == 0:\n",
        "    for f in tqdm(df.fname):\n",
        "        signal, rate=librosa.load('wavfiles/'+f,sr=16000)\n",
        "        mask = envelope(signal,rate,0.0005)\n",
        "        wavfile.write(filename='clean/'+f,rate=rate,data=signal[mask])\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F4KkI5LidgVNZsWgQO4cG2w5/U1ERvnL1qgsuSU9Pb5UQmOE4RnRbk/RoB0HK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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-97988339216c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     97\u001b[0m     \u001b[0mwav_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m \u001b[0;34m==\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m     \u001b[0msignal\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'wavfiles/'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mwav_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m44100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m     \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menvelope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignal\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.0005\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m     \u001b[0msignal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignal\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0msignals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-10-97988339216c>\u001b[0m in \u001b[0;36menvelope\u001b[0;34m(y, rate, threshold)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0my_mean\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrolling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrate\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmin_periods\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcenter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0my_mean\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'method' object is not iterable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOxarTvKt6Tj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}