"Use the inverse STFT to synthesize the separated sources:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"librosa.istft?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the columns of the matrix, $W$, otherwise known as *spectral atoms*, 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": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Bonus"
]
},
{
"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)...]`."
