Computationally-Efficient Overdetermined Blind Source Separation Based on Iterative Source Steering

This paper describesa computationally-efficient optimization algorithm for the blind source separation (BSS) of overdetermined mixtures. In the determined case, a matrix-inversion-free iterative source steering (ISS) algorithm has been proposed for estimating a square demixing matrix as a computatio...

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Veröffentlicht in:IEEE signal processing letters Jg. 29; S. 927 - 931
Hauptverfasser: Du, Yicheng, Scheibler, Robin, Togami, Masahito, Yoshii, Kazuyoshi, Kawahara, Tatsuya
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Blind source separation
Computational efficiency
Computing costs
Cost function
Demixing
fast algorithm
independent vector analysis
IP networks
Iterative algorithms
Iterative methods
Mathematical analysis
Matrix methods
Microphones
Optimization
Overdetermined blind source separation
Parameter estimation
Separation
Signal processing
Signal processing algorithms
Steering
Vector analysis
ISSN:1070-9908, 1558-2361
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Zusammenfassung:This paper describesa computationally-efficient optimization algorithm for the blind source separation (BSS) of overdetermined mixtures. In the determined case, a matrix-inversion-free iterative source steering (ISS) algorithm has been proposed for estimating a square demixing matrix as a computationally-efficient alternative to the popular iterative projection (IP) algorithm. The IP algorithm is based on source-wise (i.e., row-wise) updates of the demixing matrix, and lends itself naturally to an extension to overdetermined independent vector analysis (IVA) called OverIVA. In contrast, the ISS algorithm changes the whole demixing matrix at every update, making its extension to the overdetermined case non-trivial. In this paper, we propose a modified ISS algorithm for OverIVA fully exploiting the computational savings of ISS. We also derive an overdetermined extension of independent low-rank matrix analysis (OverILRMA) with the modified ISS algorithm. Experimental results showed that the proposed ISS-based OverIVA and OverILRMA were comparable or superior to the conventional IP-based counterparts in speech separation performance while achieving lower computational cost.
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2021.3134939