musisep.audio package

Submodules

musisep.audio.performance module

Module for calculation of the performance measures for blind source separation.

class musisep.audio.performance.MeasureAcc(n)

Bases: object

Container object for the computation of performance measures.

Parameters:n (int) – Number of instruments to consider

add(synth_signals, orig_signals)

Add portions of signals to the container.

Parameters:

• synth_signals (array_like) – Matrix with the synthesized signals in its rows
• orig_signals (array_like) – Matrix with the original signals in its rows

perms()

Obtain all the permutations of the signals and the related performance measures

Returns:

• perms (list of ndarray) – Permutations of the indices of the signals
• measures (list of ndarray) – Arrays with SDR, SIR, and SAR for the signals in rows

musisep.audio.performance.measures(synth_signals, orig_signals, size=1048576)

Compute the SDR, SIR, and SAR in all permutations of the synthesized signals.

Parameters:

• synth_signals (array_like) – Array with the synthesized signals in its rows
• orig_signals (array_like) – Array with the original signals in its rows
• size (int) – Length of the signal fragments to consider at once

Returns:

• perms (list of ndarray) – Permutations of the indices of the signals
• measures (list of ndarray) – Arrays with SDR, SIR, and SAR for the signals in rows

musisep.audio.performance.orthogonalize(signals)

Orthogonalize the given signals.

Parameters:signals (array_like) – Matrix with the signals in its rows Returns:q_matrix – Matrix with the orthogonalized signals in its rows Return type:ndarray

musisep.audio.performance.project(signals, q_matrix)

Project the given signals on the given space.

Parameters:

• signals (array_like) – Matrix with the signals in its rows
• q_matrix (array_like) – Matrix with an orthonormal basis of the space in its rows

Returns:

proj_signals – Matrix with the projected signals in its rows

Return type:

ndarray

musisep.audio.performance.select_perm(perms, measures)

Select the permutation with the highest SIR sum.

Parameters:

• perms (list of array_like) – Permutations of the indices of the signals
• measures (list of array_like) – Arrays with SDR, SIR, and SAR for the signals in rows

Returns:

• best_perm (ndarray) – Permutation of synth_signals with the lowest SIR sum
• best_measure (ndarray) – SDR, SIR, and SAR in the permutation with the lowest SIR sum

musisep.audio.specttool module

Back-end module for the Griffin-Lim algorithm.

musisep.audio.specttool.adapt_mag()

musisep.audio.specttool.unstripe()

musisep.audio.wav module

Module to handle WAV audio data.

musisep.audio.wav.read(filename)

Read WAV audio data from a file. If the data has multiple channels, they will be averaged.

Parameters:filename (string) – Name of the WAV file. Returns:

• data (ndarray) – Audio data as double array with values in [-1,1].
• samprate (int) – Sampling rate of the WAV file.

musisep.audio.wav.read_stereo(filename)

Read WAV audio data from a file. If the data has multiple channels, they will be returned as rows of the output array.

Parameters:filename (string) – Name of the WAV file. Returns:

• data (ndarray) – Audio data as double array with values in [-1,1].
• samprate (int) – Sampling rate of the WAV file.

musisep.audio.wav.unify(in_data)

Convert the input data to a double-type array with values in [-1,1]. Input type must be double, float32, int32, int16, or uint8.

Parameters:in_data (array_like) – Data to be unified Returns:out_data – Unified data. Return type:ndarray

musisep.audio.wav.write(filename, signal, samprate)

Normalize WAV audio data and write it to a file. The data type should be floating-point and must be supported by scipy.io.wavfile.

Parameters:

• filename (string) – Name of the WAV file.
• signal (array_like) – Audio data to write.
• samprate (int) – Intended sampling rate of the WAV file.

Returns:

maxval – Number by which was divided during normalization.

Return type:

scalar

Module contents