基于卷积传递函数模型的空间信息独立矢量分析

Xianrui Wang, Andreas Brendel, Gongping Huang, Yichen Yang, Walter Kellermann, Jingdong Chen
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引用次数: 2

摘要

空间信息有助于提高源分离性能。许多基于独立矢量分析(IVA)的空间信息源提取方法已经被开发出来,它们在非混响或弱混响环境下都能取得相当好的效果。然而,这些方法的性能随着混响的增加而迅速下降。其根本原因是,这些方法是基于具有rank-1假设的乘法传递函数模型推导出来的,如果混响很强,则不成立。为了解决这一问题,本文提出使用卷积传递函数(CTF)模型来提高源提取性能,并开发一种空间知情的IVA算法。仿真结果表明,即使在高混响环境下,该方法也具有良好的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatially Informed Independent vector analysis for Source Extraction based on the convolutive Transfer Function Model
Spatial information can help improve source separation performance. Numerous spatially informed source extraction methods based on the independent vector analysis (IVA) have been developed, which can achieve reasonably good performance in non- or weakly reverberant environments. However, the performance of those methods degrades quickly as the reverberation increases. The underlying reason is that those methods are derived based on the multiplicative transfer function model with a rank-1 assumption, which does not hold true if reverberation is strong. To circumvent this issue, this paper proposes to use the convolutive transfer function (CTF) model to improve the source extraction performance and develop a spatially informed IVA algorithm. Simulations demonstrate the efficacy of the developed method even in highly reverberant environments.
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