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Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria (2007)
| Venue: | IEEE Trans. On Audio, Speech and Lang. Processing |
| Citations: | 182 - 30 self |
BibTeX
@ARTICLE{Virtanen07monauralsound,
author = {Tuomas Virtanen},
title = {Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria},
journal = {IEEE Trans. On Audio, Speech and Lang. Processing},
year = {2007}
}
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Abstract
Abstract—An unsupervised learning algorithm for the separation of sound sources in one-channel music signals is presented. The algorithm is based on factorizing the magnitude spectrogram of an input signal into a sum of components, each of which has a fixed magnitude spectrum and a time-varying gain. Each sound source, in turn, is modeled as a sum of one or more components. The parameters of the components are estimated by minimizing the reconstruction error between the input spectrogram and the model, while restricting the component spectrograms to be nonnegative and favoring components whose gains are slowly varying and sparse. Temporal continuity is favored by using a cost term which is the sum of squared differences between the gains in adjacent frames, and sparseness is favored by penalizing nonzero gains. The proposed iterative estimation algorithm is initialized with random values, and the gains and the spectra are then alternatively updated using multiplicative update rules until the values converge. Simulation experiments were carried out using generated mixtures of pitched musical instrument samples and drum sounds. The performance of the proposed method was compared with independent subspace analysis and basic nonnegative matrix factorization, which are based on the same linear model. According to these simulations, the proposed method enables a better separation quality than the previous algorithms. Especially, the temporal continuity criterion improved the detection of pitched musical sounds. The sparseness criterion did not produce significant improvements. Index Terms—Acoustic signal analysis, audio source separation, blind source separation, music, nonnegative matrix factorization, sparse coding, unsupervised learning. I.
Keyphrases
sparseness criterion nonnegative matrix factorization temporal continuity monaural sound source separation sound source multiplicative update rule audio source separation independent subspace analysis unsupervised learning nonzero gain previous algorithm adjacent frame significant improvement pitched musical sound cost term generated mixture temporal continuity criterion input spectrogram magnitude spectrogram favoring component linear model iterative estimation algorithm separation quality random value component spectrogram simulation experiment pitched musical instrument sample time-varying gain input signal squared difference unsupervised learning algorithm index term acoustic signal analysis one-channel music signal reconstruction error magnitude spectrum sparse coding basic nonnegative matrix factorization source separation
