通过方向信息提升麦克风阵列分离效果
Geometrically Constrained Decentralized Independent Vector Analysis for Distributed Microphone Arrays
- 利用声源方向信息约束独立向量分析
- 在噪声环境下提升分离性能与排列一致性
- 适合分布式麦克风阵列语音分离场景
本文提出一种几何约束的去中心化独立向量分析（GC-Dec-IVA）方法，用于分布式麦克风阵列。现有去中心化独立向量分析（Dec-IVA）方法仅交换功率统计信息以利用跨阵列信息，但常因各阵列间源信号排列不一致及强跨阵列依赖性导致性能提升有限。为此，本文引入到达方向（DOA）信息，构建几何约束，缓解跨阵列排列错位问题并增强源信号对齐；同时提出新源模型以弱化跨阵列依赖性，提升在噪声环境下的排列不一致鲁棒性。实验表明，该方法在分离性能和跨阵列排列一致性方面均有显著提升。
原文摘要 · Abstract (English)
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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