Publications
論文誌 (国際) Computationally-Efficient Overdetermined Blind Source Separation Based on Iterative Source Steering
Yicheng Du (Kyoto University), Robin Scheibler, Masahito Togami, Kazuyoshi Yoshii (Kyoto University), Tatsuya Kawahara (Kyoto University)
IEEE Signal Processing Letters (IEEE SPL)
2021.12.13
This paper describes a 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.
Paper :
Computationally-Efficient Overdetermined Blind Source Separation Based on Iterative Source Steering
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