#5854. Parallel multichannel blind source separation using a spatial covariance model and nonnegative matrix factorization

July 2026publication date
Proposal available till 17-05-2025
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Journal’s subject area:
Theoretical Computer Science;
Information Systems;
Hardware and Architecture;
Software;
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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.
Keywords:
Multichannel NMF; Parallel computing; Real time; Source separation

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