Fast Multichannel NMF with Block-Diagonal Spatial Covariance Matrices for Efficient Blind Source Separation Using Distributed Microphone Arrays
Hirotaka Nishikori, Nobutaka Ito, Kouei Yamaoka, Norihiro Takamune, Hiroshi Saruwatari

TL;DR
The paper introduces a distributed FastMNMF approach with block-diagonal spatial covariance matrices, enabling efficient blind source separation across multiple microphone subarrays with reduced computation and improved performance.
Contribution
It proposes a novel distributed FastMNMF method that reduces computational cost by imposing block-diagonal structure on spatial covariance matrices, enabling scalable multi-microphone source separation.
Findings
Less computation time than conventional FastMNMF using all subarrays
Higher source-to-distortion ratio than single subarray FastMNMF
Effective in underdetermined multi-source scenarios
Abstract
Distributed microphone arrays composed of multiple subarrays enable blind source separation over a wide spatial area. Directly applying fast multichannel nonnegative matrix factorization (FastMNMF) to all subarrays can exploit observations from all subarrays, but it requires repeated inversions of large matrices spanning all microphones, causing the computational cost to increase rapidly as the number of microphones grows. In contrast, applying FastMNMF to one subarray reduces the matrix size but cannot exploit observations from other subarrays. We propose distributed FastMNMF, which imposes a block-diagonal structure on the source spatial covariance matrices, so that matrix inversions are performed within subarrays. The NMF-based source spectrogram model is shared across subarrays, allowing the method to aggregate source activity information while discarding inter-subarray covariance.…
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