[en] 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 sparsity-promoting 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 state-of-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.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Disciplines :
Electrical & electronics engineering
Author, co-author :
Dias, Thadeu; Federal University of Rio de Janeiro (UFRJ)
ALVES MARTINS, Wallace ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Biscainho, Luiz Wagner; Federal University of Rio de Janeiro (UFRJ)
External co-authors :
yes
Language :
English
Title :
Multichannel Source Separation Using Time-Deconvolutive CNMF