论文标题
基于辅助功能的算法，用于盲目提取移动扬声器
Auxiliary Function-Based Algorithm for Blind Extraction of a Moving Speaker
论文作者
论文摘要
最近，已经提出了恒定分离矢量（CSV）混合模型，用于移动源的盲源提取（BSE）。在本文中，我们通过实验验证了CSV在移动扬声器的盲提取中的适用性，并提出了一种通过修改基于辅助函数的算法进行独立向量分析而得出的新的BSE方法。此外，还针对具有部分可控制的全局收敛的方法提出了试点变体。使用{\ color {red {red}以及现实世界的声学条件}在混响和嘈杂条件下进行验证。在Chime-4语音分离和认可挑战中，它们也得到了验证。实验证实了CSV的适用性以及提出的算法的改善。
Recently, Constant Separating Vector (CSV) mixing model has been proposed for the Blind Source Extraction (BSE) of moving sources. In this paper, we experimentally verify the applicability of CSV in the blind extraction of a moving speaker and propose a new BSE method derived by modifying the auxiliary function-based algorithm for Independent Vector Analysis. Also, a piloted variant is proposed for the method with partially controllable global convergence. The methods are verified under reverberant and noisy conditions using {\color{red} simulated as well as real-world acoustic conditions}. They are also verified within the CHiME-4 speech separation and recognition challenge. The experiments corroborate the applicability of CSV as well as the improved convergence of the proposed algorithms.
