A BSS method for short utterances by a recursive solution to the permutation problem

F. Nesta, P. Svaizer, M. Omologo
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引用次数: 11

Abstract

A new approach to the permutation problem for blind source separation (BSS) in the frequency domain is presented. By means of a state-space representation, the alignment is reduced to a recursive adaptive tracking of state trajectories associated with the demixing matrices. The estimated smooth trajectories are used to initialize the independent component analysis (ICA) in order to force it to converge with a coherent permutation across the whole spectrum. Since permutations are solved with no information about the signal power, this method works also for short utterances (0.5-1 s) and in highly reverberant environment (T 60 ap 700 ms). Furthermore it is shown that the underlying frequency link, provided by the recursive state estimation, increases the accuracy in the ICA step when only few observations are available.
一种基于排列问题递归解的短话语BSS方法
提出了一种新的频域盲源分离(BSS)置换问题的求解方法。通过状态空间表示,将对齐简化为与解混矩阵相关的状态轨迹的递归自适应跟踪。估计的光滑轨迹用于初始化独立分量分析(ICA),以迫使其收敛于整个频谱上的相干排列。由于排列是在没有信号功率信息的情况下解决的,因此该方法也适用于短话语(0.5-1秒)和高混响环境(60 - 700毫秒)。此外,在只有少量观测值时,由递归状态估计提供的底层频率链接提高了ICA步骤的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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