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import numpy, scipy, matplotlib.pyplot as plt, librosa, urllib, IPython.display
Goals:
Download an audio file:
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urllib.urlretrieve?
Load an audio file:
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librosa.load?
Plot the spectrogram:
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librosa.stft?
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librosa.logamplitude?
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librosa.display.specshow?
Use NMF to decompose the spectrogram:
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librosa.decompose.decompose?
Plot the spectral profiles and temporal activations of each component individually:
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plt.subplot?
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 for more details.)
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librosa.istft?
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?
Use different audio files.
Alter the rank of the decomposition, n_components. What happens when n_components is too large? too small?
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)...].