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- Title
Parallel multichannel blind source separation using a spatial covariance model and nonnegative matrix factorization.
- Authors
Muñoz-Montoro, A. J.; Carabias-Orti, J. J.; Cortina, R.; García-Galán, S.; Ranilla, J.
- Abstract
In this paper, we present a multichannel nonnegative matrix factorization (MNMF) system for the task of source separation. We propose a novel signal model using spatial covariance matrices (SCM) where the mixing filter encodes the spatial information and the source variances are modeled using a NMF structure. Moreover, the proposed model is initialized with the estimated source direction of arrival (DoA) in order to mitigate the strong sensitivity to parameter initialization. The proposed system has been evaluated for the task of music source separation using a multichannel classical chamber music dataset showing that it is possible to reach real time in the tested scenarios by combining multi-core architectures with parallel and high-performance techniques.
- Subjects
MATRIX decomposition; BLIND source separation; NONNEGATIVE matrices; COVARIANCE matrices; PARALLEL processing; MULTICORE processors
- Publication
Journal of Supercomputing, 2021, Vol 77, Issue 10, p12143
- ISSN
0920-8542
- Publication type
Article
- DOI
10.1007/s11227-021-03771-y