论文标题
更快的IVA：基于NegentRopy的独立矢量分析的更新规则，并将其主要化原理最小化
Faster IVA: Update Rules for Independent Vector Analysis based on Negentropy and the Majorize-Minimize Principle
论文作者
论文摘要
声学信号的盲源分离算法（BSS）需要有效且快速收敛的优化策略，以适应非组织信号统计和随时间变化的声学场景。在本文中，我们从NegentRopy的角度得出了快速收敛的更新规则，该视角基于主要大小（MM）原理和特征值分解。提出的更新规则显示，由于限制了统一解散矩阵，在可比运行时以可比运行时的收敛速度以优于竞争的最先进方法。通过记录现实世界数据的实验证明了这一点。
Algorithms for Blind Source Separation (BSS) of acoustic signals require efficient and fast converging optimization strategies to adapt to nonstationary signal statistics and time-varying acoustic scenarios. In this paper, we derive fast converging update rules from a negentropy perspective, which are based on the Majorize-Minimize (MM) principle and eigenvalue decomposition. The presented update rules are shown to outperform competing state-of-the-art methods in terms of convergence speed at a comparable runtime due to the restriction to unitary demixing matrices. This is demonstrated by experiments with recorded real-world data.
