audio2img.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.io import wavfile\n",
    "fs, data = wavfile.read(\"A.wav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = data/data.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.signal import stft, istft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "r =stft(x, fs=100, nperseg=256, return_onesided=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.fftpack import dct, idct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3cb0ef43c8>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xaDUArH/HYIgNDC8JsdFjsXhz9Sg/JaMn4vpgBXtZJt80EWlIhhoAdN0Qs3QPD0RL3rHH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cb0f98d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(r[2][32:64, 60481:60481+64].imag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00075166500444117062"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[2][32:64, 60481:60481+64].imag.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.036450673476274925"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[2][:32, 60481:60481+64].imag.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3cb1458160>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/cnQ9M7NRsmD+mLs/lZLP5peF6fXcSpVvkX4B+JiZfZ9sCOwusvHnrn6N6jp1Cjjl7ofT\n+yfJAvywttGHgdfc/Q13bwBPkbXbMLcRtG+PoY4TZvabwEeBT3rxTPjA6jTIgP4csCXdnR8ju0lw\nYIDbX7I0vvwwcNzdPx9mHSD71SYYol9vcvcH3X2Tu99C1h7/4e6fZIh/jcrdXwd+aGbvTUm/DLzM\nkLYR2VDLDjObSMdfXp+hbaOkXXscAH4jPe2yA7gQhmaua2a2k2z48mPu/laYdQC4z8zGzWwz2Q3f\nbyxLIdx9YP+Bj5Dd/X0VeGiQ2+5T+T9Edml4DHgh/f8I2bjzIeAE8O/AjStd1h7q9kvAM2n6Z9IB\ndxL4B2B8pcu3yLrcBjyf2umfgHXD3EbA54DvAC8CfweMD1MbAY+Tjf83yK6g7m/XHoCRPQ33KvBt\nsqd7VrwOXdbpJNlYeR4b/jos/1Cq0yvAPctVLn1SVESkInRTVESkIhTQRUQqQgFdRKQiFNBFRCpC\nAV1EpCIU0EVEKkIBXUSkIhTQRUQq4v8B1ds9RZZJ37oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cb14bd518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h=r[2][:, 39481:39481+128]\n",
    "plt.imshow(np.moveaxis([h.imag, h.real, h.imag],0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.18217648560247296"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[2].imag.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s=istft(r[2], fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = s[1]*data.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = y.astype('int16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wavfile.write('a2.wav', fs, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0023244189607570199"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[2].real.std()"
   ]
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}