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.

翻译：本文提出了一种面向分布式麦克风阵列的几何约束去中心化独立向量分析（GC-Dec-IVA）方法。近期提出的 Dec-IVA 方法通过仅交换功率相关统计量以利用跨阵列信息实现信号分离。然而，该初始方法相比在各阵列局部应用 IVA 的改进微乎其微，主要归因于阵列间潜在的排列不一致性及其源模型所隐含的强跨阵列依赖性。为克服这些局限性，我们引入波达方向（DOA）信息推导出 GC-Dec-IVA，该方法能够缓解跨阵列的排列失配并增强源对齐效果。此外，我们引入了一种新型源模型以削弱跨阵列依赖性，从而提升算法在噪声环境中对排列不一致性的鲁棒性。实验表明，所提方法在分离性能与跨阵列排列一致性方面均有显著提升。