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A Post-filtering Algorithm for Crosstalk Subtraction in Blind Speech Separation Outputs

机译:盲语音分离输出中串扰相减的后滤波算法

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In this paper, exclusive activity periods (EAPs) are used to model crosstalks in which only one blind source separation (BSS) output signal is assumed to be active and the others are inactive. Inactive intervals of each EAP are used to estimate the crosstalk leaked from each active signal using a least squares method. Then, we use the Wiener filter to cancel the estimated crosstalks from BSS outputs. The benefit of using EAPs is to simplify the estimation of crosstalks from all other signals to the estimation of them from just one active signal in each interval. Thus, it leads to estimate and suppress crosstalks more precisely. A comparison of our method with other popular post-processing algorithms is drawn. The results show an improved performance of the proposed method over earlier approaches.
机译:在本文中,专用活动时间段(EAP)用于对串扰建模,其中仅假设一个盲源分离(BSS)输出信号为活动信号,而其他信号为非活动信号。每个EAP的非活动间隔用于使用最小二乘法估计从每个活动信号泄漏的串扰。然后,我们使用维纳滤波器从BSS输出中消除估计的串扰。使用EAP的好处是简化了所有其他信号的串扰估算,简化为每个间隔中仅一个活动信号的串扰估算。因此,它导致更精确地估计和抑制串扰。我们的方法与其他流行的后处理算法进行了比较。结果表明,与早期方法相比,该方法具有更高的性能。

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