POC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import signal\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import duplicate_audio.wavBasicRead as wbr\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./resources/wav/01-drum-loop-127-bpm.wav',\n",
       " './resources/wav/02-drum-loop-127-bpm copy.wav',\n",
       " './resources/wav/02-drum-loop-127-bpm.wav',\n",
       " './resources/wav/03-drum-loop-127-bpm.wav',\n",
       " './resources/wav/04-drum-loop-127-bpm copy.wav',\n",
       " './resources/wav/04-drum-loop-127-bpm.wav',\n",
       " './resources/wav/Slow-beat-90-bpm copy 2.wav',\n",
       " './resources/wav/Slow-beat-90-bpm copy.wav',\n",
       " './resources/wav/Slow-beat-90-bpm.wav']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wav_file_names = glob.glob(\"./resources/wav/*.wav\")\n",
    "wav_file_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.4/site-packages/scipy/io/wavfile.py:179: WavFileWarning: Chunk (non-data) not understood, skipping it.\n",
      "  WavFileWarning)\n"
     ]
    }
   ],
   "source": [
    "wavs = wbr.read_list_wav(wav_file_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0],\n",
       "       [ -3,   0],\n",
       "       [ -7,   1],\n",
       "       ..., \n",
       "       [  1,   1],\n",
       "       [-13,   5],\n",
       "       [  0,   0]], dtype=int16)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wavs[0][\"data\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_save_nchannels(in_dict, data_idx=\"data\", nchannels_idx=\"channels_n\"):\n",
    "    in_dict[nchannels_idx] = in_dict[data_idx].shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(calc_save_nchannels, wavs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****\n",
    "## calculate periodogram with Welch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_welch(in_array, fs, nperseg=256, noverlap=None):\n",
    "    return signal.welch(in_array, fs, nperseg=nperseg, noverlap=noverlap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_save_welch(in_dict, nperseg=256, noverlap=None, \n",
    "                    data_idx=\"data\", welch_idx=\"welch\", nchannels_idx=\"channels_n\", sfreq_idx=\"sampling_frequency\"):\n",
    "    # FIXME: @motjuste: what if nchannels_id does not exist?\n",
    "    in_dict[welch_idx] = []\n",
    "    for c in range(in_dict[nchannels_idx]):\n",
    "        in_dict[welch_idx].append(calc_welch(in_dict[data_idx][:, c], in_dict[sfreq_idx]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****\n",
    "Test on Temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wav_tmp = []\n",
    "from copy import deepcopy\n",
    "wav_tmp = deepcopy(wavs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,  -3,  -7, ...,   1, -13,   0], dtype=int16)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wav_tmp[\"data\"][:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44100"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wav_tmp[\"sampling_frequency\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "calc_save_welch(wav_tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110facb70>]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8+yhqG24YCvs89FDckYhIMSRy9lFBA1BLIW8PPww336wN+EQqibqPpFErVoQupHff1app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\n1FFSEBGROkoKIiJS5/8D5UxzGZvWfMwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d015b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, Pxx = wav_tmp[\"welch\"][0]\n",
    "plt.semilogy(f, Pxx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(calc_save_welch, wavs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of wav files :  9\n"
     ]
    },
    {
     "data": {
      "image/png": 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QI0uey/88YMMXcnDJw3c8VsCGL2T/GjfJzcjI3xayd5PsX9nirs5BUURErn13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\n2VbNh5Hd9tAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1117c16d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print (\"Number of wav files : \", len(wavs))\n",
    "for w in wavs:\n",
    "    f, p = w[\"welch\"][0]\n",
    "    plt.semilogy(f, p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****\n",
    "## Calculate RMS power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rms(arr, axis=None):\n",
    "    return np.sqrt(np.mean(arr**2, axis=axis))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_save_channel_rms(in_dict, data_idx=\"data\", channel_rms_idx=\"rms_channels\"):\n",
    "    in_dict[channel_rms_idx] = rms(in_dict[data_idx], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(calc_save_channel_rms, wavs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2bmY/NrOnzWwwO7/BzHaa2bbsbW3zc5sjwh4sQiOoMzV1phWlM4+2HPf5PXCZu0+a2XHA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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ac8b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(len(wavs)), [w[\"rms_channels\"][0] for w in wavs], 'o')\n",
    "plt.plot(np.arange(len(wavs)), [w[\"rms_channels\"][1] for w in wavs], 'o')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****\n",
    "Audio Fingerprinting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wav_shz = deepcopy(wavs[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "[f, t, Sxx] = signal.spectrogram(wav_shz[\"data\"][:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from skimage.feature import peak_local_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "local_max = peak_local_max(Sxx, min_distance=2, exclude_border=True, threshold_rel=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1831, 2)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local_max.shape\n",
    "# np.max(local_max, 0)\n",
    "# Sxx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lmx = np.zeros(Sxx.shape)\n",
    "lmx[local_max[:, 0], local_max[:, 1]] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x110acbb00>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib\n",
    "# plt.pcolormesh(t, f, Sxx)\n",
    "# plt.pcolormesh(t, f, lmx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10a007be0>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(np.log(Sxx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x109f0e1d0>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(lmx * 255, cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.3784877e+10"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sxx.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}