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   "source": [
    "# Sine subtraction - MusicBricks Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial will guide you through some tools for performing spectral analysis and synthesis using the Essentia library (http://www.essentia.upf.edu). \n",
    "\n",
    "This algorithm shows how to analyze the input signal, and resynthesize it again, allowing to apply new transformations directly on the spectral domain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should first install the Essentia library with Python bindings. Installation instructions are detailed here: http://essentia.upf.edu/documentation/installing.html . \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# import essentia in streaming mode\n",
    "import essentia\n",
    "import essentia.streaming as es"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After importing Essentia library, let's import other numerical and plotting tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# import matplotlib for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the parameters of the STFT workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# algorithm parameters\n",
    "params = { 'frameSize': 2048, 'hopSize': 128, 'startFromZero': False, 'sampleRate': 44100, \\\n",
    "          'maxnSines': 100,'magnitudeThreshold': -74,'minSineDur': 0.02,'freqDevOffset': 10, \\\n",
    "          'freqDevSlope': 0.001}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify input and output audio filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "inputFilename = 'singing-female.wav'\n",
    "outputFilename = 'singing-female-sinesubtraction.wav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# create an audio loader and import audio file\n",
    "out = np.array(0)\n",
    "loader = es.MonoLoader(filename = inputFilename, sampleRate = 44100)\n",
    "pool = essentia.Pool()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define algorithm chain for frame-by-frame process: \n",
    "FrameCutter -> Windowing -> FFT -> SineModelAnal -> SineSubtraction -> OutFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# algorithm instantation\n",
    "fcut = es.FrameCutter(frameSize = params['frameSize'], hopSize = params['hopSize'], startFromZero =  False);\n",
    "w = es.Windowing(type = \"blackmanharris92\");\n",
    "fft = es.FFT(size = params['frameSize']);\n",
    "smanal = es.SineModelAnal(sampleRate = params['sampleRate'], maxnSines = params['maxnSines'], magnitudeThreshold = params['magnitudeThreshold'], freqDevOffset = params['freqDevOffset'], freqDevSlope = params['freqDevSlope'])\n",
    "subtrFFTSize = min(params['frameSize']/4, 4* params['hopSize'])\n",
    "smsub = es.SineSubtraction(sampleRate = params['sampleRate'], fftSize = subtrFFTSize, hopSize = params['hopSize'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we set the algorithm network and store the processed audio samples in the output file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# analysis\n",
    "loader.audio >> fcut.signal\n",
    "fcut.frame >> w.frame\n",
    "w.frame >> fft.frame\n",
    "fft.fft >> smanal.fft\n",
    "smanal.magnitudes >> (pool, 'magnitudes')\n",
    "smanal.frequencies >> (pool, 'frequencies')\n",
    "smanal.phases >> (pool, 'phases')\n",
    "# subtraction\n",
    "fcut.frame >> smsub.frame\n",
    "smanal.magnitudes >> smsub.magnitudes\n",
    "smanal.frequencies >> smsub.frequencies\n",
    "smanal.phases >> smsub.phases\n",
    "smsub.frame >> (pool, 'frames')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we run the process that will store the output audio frames in the Pool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "essentia.run(loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we store the output audio samples in a WAV file.\n",
    "We first prepare the audio writing network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<essentia.streaming._StreamConnector instance at 0x7eff78533488>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get audio samples\n",
    "outaudio = pool['frames'].flatten() \n",
    "# instantiate audio writer and vector input\n",
    "awrite = es.MonoWriter (filename = outputFilename, sampleRate =  params['sampleRate']);\n",
    "outvector = es.VectorInput(outaudio)\n",
    "# set algorithm network \n",
    "outvector.data >> awrite.audio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we run the process for to store the output frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "essentia.run(outvector)"
   ]
  }
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