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On a Blind Speech Dereverberation Algorithm Using Multi-Channel Linear Prediction

机译:基于多通道线性预测的盲语音去混响算法

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It is well known that speech captured in a room by distant microphones suffers from distortions caused by reverberation. These distortions may seriously damage both speech characteristics and intelligibility, and consequently be harmful to many speech applications. To solve this problem, we proposed a dereverberation algorithm based on multi-channel linear prediction. The method is as follows. First we calculate prediction filters that cancel out the room reverberation but also degrade speech characteristics by causing excessive whitening of the speech. Then, we evaluate the prediction-filter degradation to compensate for the excessive whitening. As the reverberation lengthens, the compensation performance becomes worse due to computational accuracy problems. In this paper, we propose a new computation that may improve compensation accuracy when dealing with long reverberation.
机译:众所周知,远处的麦克风在房间中捕获的语音会受到混响引起的失真的影响。这些失真可能严重损害语音特性和清晰度,因此对许多语音应用有害。为了解决这个问题,我们提出了一种基于多通道线性预测的去混响算法。方法如下。首先,我们计算预测滤波器,该滤波器可以消除房间的混响,但也会由于导致语音过度白化而降低语音特性。然后,我们评估预测滤波器的降级以补偿过度的白化。随着混响的延长,由于计算精度问题,补偿性能变差。在本文中,我们提出了一种新的计算方法,可以在处理长混响时提高补偿精度。

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