Divergence optimization in nonnegative matrix factorization with spectrogram restoration for multichannel signal separation

Kitamura, Daichi; Saruwatari, Hiroshi; Nakamura, Satoshi; Takahashi, Yu; Kondo, Kazunobu; Kameoka, Hirokazu

doi:10.1109/hscma.2014.6843258

Cited by 1 publication

(1 citation statement)

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“…However, there has been no discussion on the optimal divergence for the extrapolation techniques using NMF. In this section, we experimentally analyze the extrapolation ability based on a statistical generation model of the observed data , and determine the optimal divergence for basis extrapolation for various and values [21]. In NMF decomposition, the minimization of -divergence between and corresponds to a log-likelihood maximization under the assumption of the generation model of for each [22].…”

Section: ) Optimal Divergence For Basis Extrapolation and Generationmentioning

confidence: 99%

Multichannel Signal Separation Combining Directional Clustering and Nonnegative Matrix Factorization with Spectrogram Restoration

Kitamura

Saruwatari

Kameoka

et al. 2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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In this paper, to address problems in multichannel music signal separation, we propose a new hybrid method that combines directional clustering and advanced nonnegative matrix factorization (NMF). The aims of multichannel music signal separation technology is to extract a specific target signal from observed multichannel signals that contain multiple instrumental sounds. In previous studies, various methods using NMF have been proposed, but many problems remain including poor separation accuracy and lack of robustness. To solve these problems, we propose a new supervised NMF (SNMF) with spectrogram restoration and a hybrid method that concatenates the proposed SNMF after directional clustering. Via the extrapolation of supervised spectral bases, the proposed SNMF attempts both target signal separation and reconstruction of the lost target components, which are generated by preceding directional clustering. In addition, we experimentally reveal the trade-off between separation and extrapolation abilities and propose a new scheme for adaptive divergence, where the optimal divergence can be automatically changed in each time frame according to the local spatial conditions. The results of an evaluation experiment show that our proposed hybrid method outperforms the conventional music signal separation methods.Index Terms-Multichannel signal separation, music signal processing, nonnegative matrix factorization (NMF), spectrogram restoration.

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Section: ) Optimal Divergence For Basis Extrapolation and Generationmentioning

confidence: 99%

Multichannel Signal Separation Combining Directional Clustering and Nonnegative Matrix Factorization with Spectrogram Restoration

Kitamura

Saruwatari

Kameoka

et al. 2015

IEEE/ACM Trans. Audio Speech Lang. Process.

Self Cite

View full text Add to dashboard Cite

show abstract

Divergence optimization in nonnegative matrix factorization with spectrogram restoration for multichannel signal separation

Cited by 1 publication

References 15 publications

Multichannel Signal Separation Combining Directional Clustering and Nonnegative Matrix Factorization with Spectrogram Restoration

Multichannel Signal Separation Combining Directional Clustering and Nonnegative Matrix Factorization with Spectrogram Restoration

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