Multiplicative Iterative Algorithms for NMF with Sparsity Constraints

Cichocki, Andrzej; Zdunek, Rafał; Phan, Anh Huy; Амари, Шун-ичи

doi:10.1002/9780470747278.ch3

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…In our test scenarios, the regularization did result in some slight degradation in separation, suggesting that the tonal assumption is not strictly valid-which is not surprising in real-life composed signals. Nevertheless, 1 penalty parameters are ubiquitous in regular NMF algorithms [26,27], so that the added model flexibility is welcome.…”

Section: Sdr Sirmentioning

confidence: 99%

Multichannel Source Separation Using Time-Deconvolutive CNMF

Dias

Martins

Biscainho

2020

JCIS

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This paper addresses the separation of audio sources from convolutive mixtures captured by a microphone array. We approach the problem using complex-valued non-negative matrix factorization (CNMF), and extend previous works by tailoring advanced (single-channel) NMF models, such as the deconvolutive NMF, to the multichannel factorization setup. Further, a sparsitypromoting scheme is proposed so that the underlying estimated parameters better fit the time-frequency properties inherent in some audio sources. The proposed parameter estimation framework is compatible with previous related works, and can be thought of as a step toward a more general method. We evaluate the resulting separation accuracy using a simulated acoustic scenario, and the tests confirm that the proposed algorithm provides superior separation quality when compared to a stateof-the-art benchmark. Finally, an analysis of the effects of the introduced regularization term shows that the solution is in fact steered toward a sparser representation.

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Section: Sdr Sirmentioning

confidence: 99%