用双滤波器分解降低参数量，提升麦克风阵列语音分离的稳定性。
Robust Online Overdetermined Independent Vector Analysis Based on Bilinear Decomposition
- 将长滤波器拆成两个短滤波器的双线性形式，减少参数量
- 在大阵列下保持更高分离精度，性能优于传统方法
- 适合实时语音分离场景，尤其适用于高通道麦克风系统
在线盲源分离在语音通信和人机交互中至关重要。现有方法中，过定独立向量分析（OverIVA）通过利用源信号的统计独立性及源与噪声子空间的正交性，实现了优异性能。然而，当应用于大型麦克风阵列时，参数数量迅速增加，导致在线估计精度下降。为此，本文提出将每个长分离滤波器分解为两个较短滤波器的双线性形式，从而显著降低参数量。由于两滤波器紧密耦合，设计了交替迭代投影算法进行轮流更新。仿真结果表明，在参数大幅减少的情况下，该方法仍能实现更优的性能与鲁棒性。
原文摘要 · Abstract (English)
Online blind source separation is essential for both speech communication and human-machine interaction. Among existing approaches, overdetermined independent vector analysis (OverIVA) delivers strong performance by exploiting the statistical independence of source signals and the orthogonality between source and noise subspaces. However, when applied to large microphone arrays, the number of parameters grows rapidly, which can degrade online estimation accuracy. To overcome this challenge, we propose decomposing each long separation filter into a bilinear form of two shorter filters, thereby reducing the number of parameters. Because the two filters are closely coupled, we design an alternating iterative projection algorithm to update them in turn. Simulation results show that, with far fewer parameters, the proposed method achieves improved performance and robustness.
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