用于未知说话人数量语音混合物低延迟增强的修正参数化多通道维纳滤波器
Modified Parametric Multichannel Wiener Filter \\for Low-latency Enhancement of Speech Mixtures with Unknown Number of Speakers
AI总结：
本文提出了一种低延迟在线波束形成算法 Mod-PMWF，通过修改传统参数化多通道维纳滤波器，在未知说话人数量及声学传递函数的情况下自适应增强语音混合物，并在干扰抑制比和主观听力测试中验证了其有效性。
摘要：
本文提出了一种新颖的低延迟在线波束形成（BF）算法，名为修正参数化多通道维纳滤波器，用于增强具有未知且变化的说话人数量的语音混合物。尽管传统的波束形成器（如线性约束最小方差波束形成器）可以增强语音混合物，但它们通常需要语音混合物的某些属性，例如说话人数量以及从说话人到麦克风的声学传递函数。当混合物属性不可用时，通过低延迟处理来估计这些属性具有挑战性，阻碍了波束形成器在该问题中的应用。在本文中，我们通过修改传统的参数化多通道维纳滤波器来克服这一问题。所提出的 Mod-PMWF 能够自适应地形成一种增强混合物中所有说话人的指向性图，而无需显式估计这些属性。我们的实验将展示所提出的波束形成器在干扰抑制比和主观听力测试方面的有效性。
英文摘要：
This paper introduces a novel low-latency online beamforming (BF) algorithm, named Modified Parametric Multichannel Wiener Filter (Mod-PMWF), for enhancing speech mixtures with unknown and varying number of speakers. Although conventional BFs such as linearly constrained minimum variance BF (LCMV BF) can enhance a speech mixture, they typically require such attributes of the speech mixture as the number of speakers and the acoustic transfer functions (ATFs) from the speakers to the microphones. When the mixture attributes are unavailable, estimating them by low-latency processing is challenging, hindering the application of the BFs to the problem. In this paper, we overcome this problem by modifying a conventional Parametric Multichannel Wiener Filter (PMWF). The proposed Mod-PMWF can adaptively form a directivity pattern that enhances all the speakers in the mixture without explicitly estimating these attributes. Our experiments will show the proposed BF's effectiveness in interference reduction ratios and subjective listening tests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
