voice_password_MFCC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import scipy\n",
    "from scipy.io import wavfile\n",
    "import scipy.fftpack as fft\n",
    "from scipy.signal import get_window\n",
    "import pandas\n",
    "import matplotlib.pyplot as plt\n",
    "import speechpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def normalize_audio(audio):\n",
    "    audio = audio/np.max(np.abs(audio))\n",
    "    return audio\n",
    "\n",
    "def frame_audio(audio, FFT_size = 2048, hop_size = 512, sample_rate=44100):\n",
    "    audio = np.pad(audio, int(FFT_size/2), mode='reflect')\n",
    "    frame_len = np.round(sample_rate * hop_size / 1000).astype(int)\n",
    "    frame_num = int((len(audio) - FFT_size) / frame_len) + 1\n",
    "    frames = np.zeros((frame_num, FFT_size))\n",
    "    for n in range(frame_num):\n",
    "        frames[n] = audio[n*frame_len : n*frame_len+FFT_size]\n",
    "    return frames\n",
    "\n",
    "def freq_to_mel(freq):\n",
    "    return 2595.0 * np.log10(1.0 + freq / 700.0)\n",
    "\n",
    "def met_to_freq(mels):\n",
    "    return 700.0 * (10.0**(mels / 2595.0) - 1.0)\n",
    "\n",
    "def get_filter_points(fmin, fmax, mel_filter_num, FFT_size, sample_rate=44100):\n",
    "    fmin_mel = freq_to_mel(fmin)\n",
    "    fmax_mel = freq_to_mel(fmax)    \n",
    "    mels = np.linspace(fmin_mel, fmax_mel, num=mel_filter_num+2)\n",
    "    freqs = met_to_freq(mels)    \n",
    "    return np.floor((FFT_size + 1) / sample_rate * freqs).astype(int), freqs\n",
    "\n",
    "def get_filters(filter_points, FFT_size):\n",
    "    filters = np.zeros((len(filter_points)-2, int((FFT_size/2)+1)))\n",
    "    for n in range(len(filter_points)-2):\n",
    "        filters[n, filter_points[n] : filter_points[n + 1]] = np.linspace(0, 1, filter_points[n + 1] - filter_points[n])\n",
    "        filters[n, filter_points[n + 1] : filter_points[n + 2]] = np.linspace(1, 0, filter_points[n + 2] - filter_points[n + 1])\n",
    "    return filters\n",
    "\n",
    "def dct(dct_filter_num, filter_len):\n",
    "    basis = np.empty((dct_filter_num,filter_len))\n",
    "    basis[0, :] = 1.0 / np.sqrt(filter_len)\n",
    "    \n",
    "    samples = np.arange(1, 2 * filter_len, 2) * np.pi / (2.0 * filter_len)\n",
    "\n",
    "    for i in range(1, dct_filter_num):\n",
    "        basis[i, :] = np.cos(i * samples) * np.sqrt(2.0 / filter_len)\n",
    "        \n",
    "    return basis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainpath = \"C:/Users/Dataset/\"\n",
    "filename = [\"DataLatih/Lock/Lock\",\"DataLatih/Unlock/Unlock\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false,
    "tags": [
     "outputPrepend"
    ]
   },
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     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "#MFCC Data latih\n",
    "count = 0\n",
    "datacount = 720*2 #jumlah data latih\n",
    "fiturmean = np.empty((40+1, datacount))\n",
    "for kategori in range(2):\n",
    "    for i in range(720):\n",
    "        #open file & get sample rate\n",
    "        sample_rate, audio = wavfile.read(trainpath + filename[kategori] + \" (\"+ str(i+1) +\").wav\")\n",
    "        print(filename[kategori] + \" (\"+ str(i+1) +\").wav\")\n",
    "\n",
    "        #normalize audio\n",
    "        if (len(audio.shape) > 1):\n",
    "            audio1 = normalize_audio(audio[:,0])\n",
    "        else:\n",
    "            audio1 = normalize_audio(audio)\n",
    "\n",
    "        #crop the blank moment \n",
    "        threshold=0.1\n",
    "        awal = 0\n",
    "        for x in range (len(audio1)):\n",
    "            if np.abs(audio1[x]) >= threshold:\n",
    "                awal=x #Data sinyal ke-x sebagai sinyal awal\n",
    "                break\n",
    "        audiohasil=audio1[awal:len(audio1)]#mengambil data sinyal mulai dari data ke-x sd data terakhir\n",
    "\n",
    "        for x in range (len(audiohasil)):\n",
    "            if np.abs(audiohasil[x]) >=threshold:\n",
    "                akhir=x #Data sinyal ke-x yg terakhir\n",
    "        audiohasil2=audiohasil[0:akhir]\n",
    "\n",
    "        #audio framing\n",
    "        hop_size = 512 \n",
    "        FFT_size = 2048\n",
    "        audio_framed = frame_audio(audiohasil2, FFT_size=FFT_size, hop_size=hop_size, sample_rate=sample_rate)\n",
    "\n",
    "        #windowing\n",
    "        window = get_window(\"hamming\", FFT_size, fftbins=True)\n",
    "        audio_win = audio_framed * window\n",
    "\n",
    "        #fft\n",
    "        audio_winT = np.transpose(audio_win)\n",
    "        audio_fft = np.empty((int(1 + FFT_size // 2), audio_winT.shape[1]), dtype=np.complex64, order='F')\n",
    "        for n in range(audio_fft.shape[1]):\n",
    "            audio_fft[:, n] = fft.fft(audio_winT[:, n], axis=0)[:audio_fft.shape[0]]\n",
    "        audio_fft = np.transpose(audio_fft)\n",
    "\n",
    "        #power spectrum\n",
    "        audio_power = np.square(np.abs(audio_fft))\n",
    "\n",
    "        #mel filter bank\n",
    "        freq_min = 0\n",
    "        freq_high = sample_rate / 2\n",
    "        mel_filter_num = 10\n",
    "        filter_points, mel_freqs = get_filter_points(freq_min, freq_high, mel_filter_num, FFT_size, sample_rate)\n",
    "        filters = get_filters(filter_points, FFT_size)\n",
    "        enorm = 2.0 / (mel_freqs[2:mel_filter_num+2] - mel_freqs[:mel_filter_num])\n",
    "        filters *= enorm[:, np.newaxis]\n",
    "\n",
    "        audio_filtered = np.dot(filters, np.transpose(audio_power))\n",
    "        audio_log = 10.0 * np.log10(audio_filtered)\n",
    "\n",
    "        #DCT & cepstral coefficient\n",
    "        dct_filter_num = 40\n",
    "        dct_filters = dct(dct_filter_num, mel_filter_num)\n",
    "        cepstral_coefficents = np.dot(dct_filters, audio_log)\n",
    "\n",
    "        #normalizing cepstral coefficient value using CMVN method ((Xn-Xmean)/Variance)\n",
    "        cepstral_coefficents = speechpy.processing.cmvn(cepstral_coefficents,True)\n",
    "\n",
    "        #ceptral coeffienct dirata2kan\n",
    "        for xpos in range(len(cepstral_coefficents)):\n",
    "            sigmax = 0\n",
    "            for xn in cepstral_coefficents[xpos,:]:\n",
    "                sigmax += xn\n",
    "            fiturmean[xpos,count] = sigmax/len(np.transpose(cepstral_coefficents))\n",
    "        fiturmean[-1,count] = kategori\n",
    "\n",
    "        count+=1\n",
    "indextable = []\n",
    "for i in range(40):\n",
    "    indextable.append(\"fitur\" + str(i+1))\n",
    "indextable.append(\"klasifikasi\")\n",
    "\n",
    "df = pandas.DataFrame(np.transpose(fiturmean),columns=indextable)\n",
    "df.to_excel(\"ekstraksilatih.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "#buat data uji\n",
    "count = 0\n",
    "datacount = 180*2 #jumlah data latih\n",
    "fiturmean = np.empty((40+1, datacount))\n",
    "for kategori in range(2):\n",
    "    for i in range(180):\n",
    "        #open file & get sample rate\n",
    "        sample_rate, audio = wavfile.read(trainpath + filename[kategori] + \" (\"+ str(i+721) +\").wav\")\n",
    "        print(filename[kategori] + \" (\"+ str(i+721) +\").wav\")\n",
    "\n",
    "        #normalisasi audio\n",
    "        if (len(audio.shape) > 1):\n",
    "            audio1 = normalize_audio(audio[:,0]) \n",
    "        else:\n",
    "            audio1 = normalize_audio(audio)\n",
    "\n",
    "        #memotong bagian silence\n",
    "        threshold=0.1\n",
    "        awal = 0\n",
    "        for x in range (len(audio1)):\n",
    "            if np.abs(audio1[x]) >= threshold:\n",
    "                awal=x \n",
    "                break\n",
    "        audiohasil=audio1[awal:len(audio1)]\n",
    "\n",
    "        for x in range (len(audiohasil)):\n",
    "            if np.abs(audiohasil[x]) >=threshold:\n",
    "                akhir=x \n",
    "        audiohasil2=audiohasil[0:akhir]\n",
    "\n",
    "        #audio framing\n",
    "        hop_size = 512\n",
    "        FFT_size = 2048\n",
    "        audio_framed = frame_audio(audiohasil2, FFT_size=FFT_size, hop_size=hop_size, sample_rate=sample_rate)\n",
    "\n",
    "        #windowing\n",
    "        window = get_window(\"hamming\", FFT_size, fftbins=True)\n",
    "        audio_win = audio_framed * window\n",
    "\n",
    "        #fft\n",
    "        audio_winT = np.transpose(audio_win)\n",
    "        audio_fft = np.empty((int(1 + FFT_size // 2), audio_winT.shape[1]), dtype=np.complex64, order='F')\n",
    "        for n in range(audio_fft.shape[1]):\n",
    "            audio_fft[:, n] = fft.fft(audio_winT[:, n], axis=0)[:audio_fft.shape[0]]\n",
    "        audio_fft = np.transpose(audio_fft)\n",
    "\n",
    "        #power spectrum\n",
    "        audio_power = np.square(np.abs(audio_fft))\n",
    "\n",
    "        #creating mel filter bank\n",
    "        freq_min = 0\n",
    "        freq_high = sample_rate / 2\n",
    "        mel_filter_num = 10\n",
    "        filter_points, mel_freqs = get_filter_points(freq_min, freq_high, mel_filter_num, FFT_size, sample_rate)\n",
    "        filters = get_filters(filter_points, FFT_size)\n",
    "        enorm = 2.0 / (mel_freqs[2:mel_filter_num+2] - mel_freqs[:mel_filter_num])\n",
    "        filters *= enorm[:, np.newaxis]\n",
    "\n",
    "        audio_filtered = np.dot(filters, np.transpose(audio_power))\n",
    "        audio_log = 10.0 * np.log10(audio_filtered)\n",
    "\n",
    "        #create cepstral coefficient\n",
    "        dct_filter_num = 40\n",
    "        dct_filters = dct(dct_filter_num, mel_filter_num)\n",
    "        cepstral_coefficents = np.dot(dct_filters, audio_log)\n",
    "\n",
    "        #normalizing cepstral coefficient value using CMVN method ((Xn-Xmean)/Variance)\n",
    "        cepstral_coefficents = speechpy.processing.cmvn(cepstral_coefficents,True)\n",
    "\n",
    "        #ceptral coeffienct dirata2kan\n",
    "        for xpos in range(len(cepstral_coefficents)):\n",
    "            sigmax = 0\n",
    "            for xn in cepstral_coefficents[xpos,:]:\n",
    "                sigmax += xn\n",
    "            fiturmean[xpos,count] = sigmax/len(np.transpose(cepstral_coefficents))\n",
    "        fiturmean[-1,count] = kategori\n",
    "\n",
    "        count+=1\n",
    "indextable = []\n",
    "for i in range(40):\n",
    "    indextable.append(\"fitur\" + str(i+1))\n",
    "indextable.append(\"klasifikasi\")\n",
    "\n",
    "df = pandas.DataFrame(np.transpose(fiturmean),columns=indextable)\n",
    "df.to_excel(\"ekstraksiuji.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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