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An approach to blind source separation based on temporal structure of speech signals

机译:基于语音信号时间结构的盲源分离方法

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In this paper, we introduce a new technique for blind source separation of speech signals. We focus on the temporal structure of the signals. The idea is to apply the decorrelation method proposed by Molgedey and Schuster in the time-frequency domain. Since we are applying separation algorithm on each frequency separately, we have to solve the amplitude and permutation ambiguity properly to reconstruct the separated signals. For solving the amplitude ambiguity, we use the matrix inversion and for the permutation ambiguity, we introduce a method based on the temporal structure of speech signals. We show some results of experiments with both artificially controlled data and speech data recorded in the real environment.
机译:在本文中,我们介绍了一种用于语音信号盲源分离的新技术。我们专注于信号的时间结构。想法是将Molgedey和Schuster提出的去相关方法应用于时频域。由于我们分别在每个频率上应用分离算法,因此必须适当解决幅度和置换模糊性,以重建分离的信号。为了解决幅度模糊性,我们使用矩阵求逆,对于排列歧义,我们引入一种基于语音信号的时间结构的方法。我们展示了在真实环境中记录的人工控制数据和语音数据的实验结果。

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