nmf_source_separation.ipynb

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   "source": [
    "import numpy, scipy, matplotlib.pyplot as plt, librosa, urllib, IPython.display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[&larr; Back to Index](index.html)"
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   "cell_type": "markdown",
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   "source": [
    "# Exercise: Source Separation using NMF"
   ]
  },
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   "source": [
    "Goals:\n",
    "\n",
    "1. Separate sources using NMF.\n",
    "2. Analyze and classify separated sources."
   ]
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   "cell_type": "markdown",
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   "source": [
    "Download an audio file:"
   ]
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   "source": [
    "urllib.urlretrieve?"
   ]
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   "cell_type": "markdown",
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   "source": [
    "Load an audio file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "librosa.load?"
   ]
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   "cell_type": "markdown",
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   "source": [
    "Plot the spectrogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "librosa.stft?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "librosa.logamplitude?"
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    "collapsed": false
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    "librosa.display.specshow?"
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    "Use NMF to decompose the spectrogram:"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "librosa.decompose.decompose?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the spectral profiles and temporal activations of each component individually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "plt.subplot?"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the inverse STFT to synthesize the separated sources. (`librosa.istft` expects a full, complex-valued spectrogram. Therefore, you will need to include the phase of the original spectrogram in your reconstruction. See [the notebook on NMF](nmf.html) for more details.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "librosa.istft?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the reconstructed outputs as inputs into the kick/snare classifier that you created in an earlier exercise. Observe the results; are you able to automatically classify the separated sources?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For Further Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use different audio files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alter the rank of the decomposition, `n_components`. What happens when `n_components` is too large? too small?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NMF is a useful preprocessor for MIR tasks such as music transcription. Using the steps above, build your own simple transcription system that returns a sequence of note events, `[(onset time, class label, volume/gain)...]`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[&larr; Back to Index](index.html)"
   ]
  }
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