madmom.audio.chroma¶

This module contains chroma related functionality.

class madmom.audio.chroma.DeepChromaProcessor(fmin=65, fmax=2100, unique_filters=True, models=None, **kwargs)[source]¶

Compute chroma vectors from an audio file using a deep neural network that focuses on harmonically relevant spectral content.

Parameters:

fmin : int, optional

Minimum frequency of the filterbank [Hz].

fmax : float, optional

Maximum frequency of the filterbank [Hz].

unique_filters : bool, optional

Indicate if the filterbank should contain only unique filters, i.e. remove duplicate filters resulting from insufficient resolution at low frequencies.

models : list of filenames, optional

List of model filenames.

Notes

Provided model files must be compatible with the processing pipeline and the values of fmin, fmax, and unique_filters. The general use case for the models parameter is to use a specific model instead of an ensemble of all models.

The models shipped with madmom differ slightly from those presented in the paper (less hidden units, narrower frequency band for spectrogram), but achieve similar results.

References

[R1]	Filip Korzeniowski and Gerhard Widmer, “Feature Learning for Chord Recognition: The Deep Chroma Extractor”, Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), 2016.

Examples

Extract a chroma vector using the deep chroma extractor:

>>> dcp = DeepChromaProcessor()
>>> chroma = dcp('tests/data/audio/sample2.wav')
>>> chroma  
array([[ 0.01317,  0.00721,  ...,  0.00546,  0.00943],
       [ 0.36809,  0.01314,  ...,  0.02213,  0.01838],
       ...,
       [ 0.1534 ,  0.06475,  ...,  0.00896,  0.05789],
       [ 0.17513,  0.0729 ,  ...,  0.00945,  0.06913]], dtype=float32)
>>> chroma.shape
(41, 12)