Geometrically Constrained Decentralized Independent Vector Analysis for Distributed Microphone Arrays
For researchers working on distributed microphone array signal processing, this method addresses permutation inconsistency and cross-array dependency, offering a practical improvement over existing decentralized approaches.
The paper proposes GC-Dec-IVA to improve source separation in distributed microphone arrays by incorporating DOA information and a new source model, achieving better separation performance and cross-array permutation consistency compared to prior Dec-IVA.
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.