Geometrically Constrained Decentralized Independent Vector Analysis for Distributed Microphone Arrays

Changda Chen; Yichen Yang; Wei Liu; Bing Zhu; Gongping Huang; Shoji Makino; Shuai Wang

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arXiv · 2606.15826

Geometrically Constrained Decentralized Independent Vector Analysis for Distributed Microphone Arrays

Changda Chen·Yichen Yang·Wei Liu·Bing Zhu·Gongping Huang·Shoji Makino·Shuai Wang

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Abstract

This paper proposes a geometrically constrained decentralized independent vector analysis (GC-Dec-IVA) method for distributed microphone arrays. Recently proposed Dec-IVA method enables source separation by exchanging only power-related statistics to exploit cross-array information. However, this initial attempt often provides negligible improvement over applying IVA locally at each array, mainly due to the potential permutation inconsistency among arrays and the strong cross-array dependency implied by its source model. To address these limitations, we incorporate direction-of-arrival (DOA) information to derive GC-Dec-IVA, which mitigates permutation mismatch across arrays and enhances source alignment. Furthermore, a new source model is introduced to weaken cross-array dependency, improving robustness against permutation inconsistency in noisy environments. Experiments show the proposed method improves both the separation performance and cross-array permutation consistency.

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BibTeX RIS

Changda Chen, Yichen Yang, Wei Liu, Bing Zhu, Gongping Huang, Shoji Makino, Shuai Wang. 2026-06-14. Geometrically Constrained Decentralized Independent Vector Analysis for Distributed Microphone Arrays. https://arxiv.org/abs/2606.15826

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