论文标题
加速基于辅助功能的独立矢量分析
Accelerating Auxiliary Function-based Independent Vector Analysis
论文作者
论文摘要
独立的矢量分析（IVA）是音频信号复杂混合物盲源分离（BS）的有效方法。作为基于IVA的BSS算法的实际实现，已经提出了基于大型最小化（MM）原理的所谓的Auxiva更新规则，该规则允许快速和计算有效地对IVA成本函数进行有效的优化。但是，对于许多实时应用程序，非常需要更新IVA的规则。为此，我们研究了在没有额外的计算成本的情况下加速Auxiva更新规则的收敛的技术。在代表现实世界情景的实验中验证了所提出方法的功效。
Independent Vector Analysis (IVA) is an effective approach for Blind Source Separation (BSS) of convolutive mixtures of audio signals. As a practical realization of an IVA-based BSS algorithm, the so-called AuxIVA update rules based on the Majorize-Minimize (MM) principle have been proposed which allow for fast and computationally efficient optimization of the IVA cost function. For many real-time applications, however, update rules for IVA exhibiting even faster convergence are highly desirable. To this end, we investigate techniques which accelerate the convergence of the AuxIVA update rules without extra computational cost. The efficacy of the proposed methods is verified in experiments representing real-world acoustic scenarios.
