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.

翻译：在线盲源分离对于语音通信和人机交互至关重要。在现有方法中，过定独立向量分析（OverIVA）通过利用源信号的统计独立性以及源子空间与噪声子空间之间的正交性，能够提供优异的性能。然而，当应用于大型麦克风阵列时，参数数量迅速增加，这可能降低在线估计的准确性。为了克服这一挑战，我们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，从而减少参数数量。由于这两个滤波器紧密耦合，我们设计了一种交替迭代投影算法来依次更新它们。仿真结果表明，在参数数量大幅减少的情况下，所提方法实现了更好的性能和鲁棒性。