ThetaOscillator-SyllableSegmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oscillator-based speech syllabification algorithm\n",
    "\n",
    "Python implementation of \n",
    "> Räsänen, Doyle & Frank (2018). Pre-linguistic segmentation of speech into syllable-like units. Cognition, Vol. 171, pp. 130–150.\n",
    "\n",
    "Based on the Matlab implementation available here: https://github.com/orasanen/thetaOscillator\n",
    "\n",
    "The peak detection algorithm is from: https://gist.github.com/endolith/250860#file-peakdetect-py\n",
    "\n",
    "The gammatone filterbank uses: http://detly.github.io/gammatone/filters.html \n",
    "\n",
    "\n",
    "**License:** The work is under a GNU General Public License v3.0 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Peak detection function\n",
    "Original code:  https://gist.github.com/endolith/250860#file-peakdetect-py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from numpy import NaN, Inf, arange, isscalar, asarray, array\n",
    "\n",
    "def peakdet(v, delta, x = None):\n",
    "    \"\"\"\n",
    "    Converted from MATLAB script at http://billauer.co.il/peakdet.html\n",
    "    \n",
    "    Returns two arrays\n",
    "    \n",
    "    function [maxtab, mintab]=peakdet(v, delta, x)\n",
    "    %PEAKDET Detect peaks in a vector\n",
    "    %        [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local\n",
    "    %        maxima and minima (\"peaks\") in the vector V.\n",
    "    %        MAXTAB and MINTAB consists of two columns. Column 1\n",
    "    %        contains indices in V, and column 2 the found values.\n",
    "    %      \n",
    "    %        With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices\n",
    "    %        in MAXTAB and MINTAB are replaced with the corresponding\n",
    "    %        X-values.\n",
    "    %\n",
    "    %        A point is considered a maximum peak if it has the maximal\n",
    "    %        value, and was preceded (to the left) by a value lower by\n",
    "    %        DELTA.\n",
    "    \n",
    "    % Eli Billauer, 3.4.05 (Explicitly not copyrighted).\n",
    "    % This function is released to the public domain; Any use is allowed.\n",
    "    \n",
    "    \"\"\"\n",
    "    maxtab = []\n",
    "    mintab = []\n",
    "       \n",
    "    if x is None:\n",
    "        x = arange(len(v))\n",
    "    v = asarray(v)\n",
    "    if len(v) != len(x):\n",
    "        sys.exit('Input vectors v and x must have same length')\n",
    "    \n",
    "    if not isscalar(delta):\n",
    "        sys.exit('Input argument delta must be a scalar')\n",
    "    \n",
    "    if delta <= 0:\n",
    "        sys.exit('Input argument delta must be positive')\n",
    "    \n",
    "    mn, mx = Inf, -Inf\n",
    "    mnpos, mxpos = NaN, NaN\n",
    "    \n",
    "    lookformax = True\n",
    "    for i in arange(len(v)):\n",
    "        this = v[i]\n",
    "        if this > mx:\n",
    "            mx = this\n",
    "            mxpos = x[i]\n",
    "        if this < mn:\n",
    "            mn = this\n",
    "            mnpos = x[i]\n",
    "        \n",
    "        if lookformax:\n",
    "            if this < mx-delta:\n",
    "                maxtab.append((mxpos, mx))\n",
    "                mn = this\n",
    "                mnpos = x[i]\n",
    "                lookformax = False\n",
    "        else:\n",
    "            if this > mn+delta:\n",
    "                mintab.append((mnpos, mn))\n",
    "                mx = this\n",
    "                mxpos = x[i]\n",
    "                lookformax = True\n",
    "\n",
    "    return array(maxtab), array(mintab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ThetaOscillator function\n",
    "\n",
    "Original Matlab code here: https://github.com/orasanen/thetaOscillator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def thetaOscillator(ENVELOPE, f=5, Q=0.5, thr=0.025, verbose=1):\n",
    "    \n",
    "    N = 10;# How many most energetic bands to use (default = 8)\n",
    "\n",
    "    if N > ENVELOPE.size:\n",
    "        print ('WARNING: Input dimensionality smaller than the N parameter. Using all frequency bands.');\n",
    "    # %% Find correct delay compensation (readily tabulated)\n",
    "\n",
    "    a = np.array([[72, 34, 22, 16, 12, 9, 8, 6, 5, 4, 3, 3, 2, 2, 1, 0, 0, 0, 0, 0],\n",
    "        [107, 52, 34, 25, 19, 16, 13, 11, 10, 9, 8, 7, 6, 5, 5, 4, 4, 4, 3, 3],\n",
    "        [129, 64, 42, 31, 24, 20, 17, 14, 13, 11, 10, 9, 8, 7, 7, 6, 6, 5, 5, 4],\n",
    "        [145, 72, 47, 35, 28, 23, 19, 17, 15, 13, 12, 10, 9, 9, 8, 7, 7, 6, 6, 5],\n",
    "        [157, 78, 51, 38, 30, 25, 21, 18, 16, 14, 13, 12, 11, 10, 9, 8, 8, 7, 7, 6],\n",
    "        [167, 83, 55, 41, 32, 27, 23, 19, 17, 15, 14, 12, 11, 10, 10, 9, 8, 8, 7, 7],\n",
    "        [175, 87, 57, 43, 34, 28, 24, 21, 18, 16, 15, 13, 12, 11, 10, 9, 9, 8, 8, 7],\n",
    "        [181, 90, 59, 44, 35, 29, 25, 21, 19, 17, 15, 14, 13, 12, 11, 10, 9, 9, 8, 8],\n",
    "        [187, 93, 61, 46, 36, 30, 25, 22, 19, 17, 16, 14, 13, 12, 11, 10, 10, 9, 8, 8],\n",
    "        [191, 95, 63, 47, 37, 31, 26, 23, 20, 18, 16, 15, 13, 12, 11, 11, 10, 9, 9, 8]]);\n",
    "\n",
    "    i1 = max(0,min(10,round(Q*10)));\n",
    "    i2 = max(0,min(20,round(f)));\n",
    "\n",
    "    delay_compensation = a[i1-1][i2-1];\n",
    "    \n",
    "    # Get oscillator mass\n",
    "    T = 1./f;   # Oscillator period\n",
    "    k = 1;      # Fix spring constant k = 1, define only mass\n",
    "    b = 2*np.pi/T; \n",
    "    m = k/b**2;  # Mass of the oscillator\n",
    "\n",
    "    \n",
    "    # % Get oscillator damping coefficient\n",
    "    c = np.sqrt(m*k)/Q;\n",
    "    \n",
    "    if(verbose):\n",
    "        print('Oscillator Q-value: %0.4f, center frequency: %0.1f Hz, bandwidth: %0.1f Hz.\\n'%(Q,1/T,1/T/Q));\n",
    "\n",
    "    # Do zero padding\n",
    "    e = np.transpose(ENVELOPE);\n",
    "    e = np.vstack((e,np.zeros((500, e.shape[1]))))\n",
    "    F = e.shape[1] # Number of frequency channels\n",
    "    \n",
    "    # Get oscillator amplitudes as a function of time\n",
    "    x = np.zeros((e.shape[0],F));\n",
    "    a = np.zeros((e.shape[0],F));\n",
    "    v = np.zeros((e.shape[0],F));\n",
    "                \n",
    "    for t in range(1,e.shape[0]):        \n",
    "        for cf in range(F):\n",
    "            f_up = e[t,cf]; # driving positive force \n",
    "            f_down = -k*x[t-1,cf]-c*v[t-1,cf];\n",
    "            f_tot = f_up+f_down; # Total force\n",
    "            # Get acceleration from force\n",
    "            a[t,cf] = f_tot/m;\n",
    "                \n",
    "            # Get velocity from acceleration\n",
    "            v[t,cf] = v[t-1,cf]+a[t,cf]*0.001; # assumes 1000 Hz sampling\n",
    "            # Get position from velocity\n",
    "            x[t,cf] = x[t-1,cf]+v[t,cf]*0.001;                \n",
    "\n",
    "    \n",
    "    # Perform group delay correction by removing samples from the\n",
    "    # beginning and adding zeroes to the end\n",
    "    for f in range(F):\n",
    "        if(delay_compensation):\n",
    "            x[:,f] = np.append(x[delay_compensation:,f],np.zeros((delay_compensation,1)));\n",
    "    \n",
    "    x = x[:-500]; # Remove zero-padding\n",
    "    \n",
    "    # Combine N most energetic bands to get sonority envelope\n",
    "    tmp = x;\n",
    "    tmp = tmp-np.min(tmp)+0.00001;\n",
    "    x = np.zeros((tmp.shape[0],1));\n",
    "\n",
    "    for zz in range(tmp.shape[0]):\n",
    "        sort_tmp = np.sort(tmp[zz,:],axis=0)[::-1];  \n",
    "        x[zz] = sum((np.log10(sort_tmp[:N])));                \n",
    "\n",
    "    # Scale sonority envelope between 0 and 1\n",
    "    x = x-np.min(x);\n",
    "    x = x/np.max(x);\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the syllable detection over an audio signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gammatone Python module\n",
    "#!pip3 install git+https://github.com/detly/gammatone.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oscillator Q-value: 0.5000, center frequency: 5.0 Hz, bandwidth: 10.0 Hz.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import librosa\n",
    "import numpy as np\n",
    "import gammatone.filters\n",
    "from scipy.signal import hilbert\n",
    "\n",
    "# Generate Gammatone filterbank center frequencies (log-spacing)\n",
    "minfreq = 50\n",
    "maxfreq = 7500\n",
    "bands = 20\n",
    "\n",
    "cfs = np.zeros((bands,1));\n",
    "const = (maxfreq/minfreq)**(1/(bands-1));\n",
    "#const = 1.3018\n",
    "\n",
    "cfs[0] = 50;\n",
    "for k in range(bands-1):\n",
    "    cfs[k+1] = cfs[k]*const;\n",
    "    \n",
    "\n",
    "# Read the audio data\n",
    "wav_data, fs = librosa.load('deasemenea.wav')\n",
    "wav_data = librosa.resample(wav_data,fs,16000);\n",
    "fs = 16000;\n",
    "# Compute gammatone envelopes and downsample to 1000 Hz\n",
    "coefs = gammatone.filters.make_erb_filters(fs, cfs, width=1.0)\n",
    "filtered_signal = gammatone.filters.erb_filterbank(wav_data, coefs)\n",
    "hilbert_envelope = np.abs(hilbert(filtered_signal))\n",
    "env = librosa.resample(hilbert_envelope,fs,1000);\n",
    "\n",
    "# Run oscillator-based segmentation\n",
    "Q_value = 0.5;  # Q-value of the oscillator, default = 0.5 = critical damping\n",
    "center_frequency = 5; # in Hz\n",
    "threshold = 0.01;\n",
    "\n",
    "# Get the sonority function\n",
    "outh = thetaOscillator(env,center_frequency,Q_value,threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'First 10 Gammatone Filter Envelopes')"
      ]
     },
     "execution_count": 5,
     "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": [
    "import matplotlib.pylab as pl\n",
    "%matplotlib inline\n",
    "pl.plot(env[1:10].transpose());\n",
    "pl.title('First 10 Gammatone Filter Envelopes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detect the peaks and valleys of the sonority function\n",
    "peaks,valleys = peakdet(outh,threshold); \n",
    "if len(valleys) and len(peaks):\n",
    "    valley_indices = valleys[:,0]\n",
    "    peak_indices = peaks[:,0]\n",
    "\n",
    "    # Add signal onset if not detected by valley picking \n",
    "    if valley_indices[0] > 50:\n",
    "        valley_indices = np.insert(valley_indices,0,0)\n",
    "    if valley_indices[-1] < env.shape[1] -50:\n",
    "        valley_indices = np.append(valley_indices,env.shape[1])\n",
    "else:\n",
    "    valley_indices = [0, len(env)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the results\n",
    "pl.figure(figsize=(18,8))\n",
    "time_axis = [i*1./fs for i in range(len(wav_data))]\n",
    "pl.plot(time_axis, wav_data)\n",
    "pl.xlim((-0.01, (len(wav_data))*1./fs+0.01))\n",
    "time_axis = [i*1./1000 for i in range(len(outh))]\n",
    "pl.plot(time_axis, outh)\n",
    "\n",
    "for vi in valley_indices:\n",
    "    pl.axvline(vi*1./1000, ymin=0, ymax=1, color='r', linestyle='dashed')\n",
    "    \n",
    "## Plot syll nuclei\n",
    "# for pi in peak_indices:\n",
    "#     pl.axvline(pi*1./1000, ymin=0, ymax=1, color = 'g', linestyle='dashed')\n",
    "\n",
    "## Plot true boundaries\n",
    "# zz = [0, 0.264, 0.446, 0.573, 0.7854];\n",
    "# for z in zz:\n",
    "#    pl.axvline(z, ymin=0, ymax=1, color = 'k', linestyle='dashed')\n",
    "    \n",
    "    \n",
    "pl.legend(['signal', 'sonority', 'estimated syll boundaries']);\n",
    "pl.title('Theta oscillator based syllable boundary detection');\n",
    "pl.xlabel ('Time [s]')\n",
    "pl.savefig('output.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython\n",
    "IPython.display.Audio(wav_data, rate=fs)"
   ]
  }
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