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Acoustic Echo Cancellation During Doubletalk Using Convolutive Blind Source Separation of Signals Having Temporal Dependence

机译:双甘甘串期间的声学回声消除使用具有时间依赖的信号的卷曲盲源分离

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This paper describes a new algorithm for acoustic echo cancellation during doubletalk or, more precisely, acoustic echo separation, based on blind source separation (BSS) of convolutively mixed signals. The signal model assumes independence between sources, but temporal dependence between time samples, specifically that the vector signals have first-order Markov dependence. The source separation is done using a maximum likelihood approach. The source separation does not always provide separation, because of too many degrees of freedom on the separation. However, when applied to the acoustic echo cancellation problem, the constraints of the echo system neatly solve this problem. An example shows that acoustic echoes can be cleanly separated during doubletalk.
机译:本文介绍了一种基于旋转混合信号的盲源分离(BSS)的双甘甘术期间的声学回声消除的新算法,或者更精确地,声回声分离。信号模型假设源之间的独立性,但时间样本之间的时间依赖性,特别是矢量信号具有一阶马尔可夫依赖性。使用最大可能性方法完成源分离。由于分离的自由度太多,源分离并不总是提供分离。然而,当应用于声学回声消除问题时,回声系统的约束整齐地解决了这个问题。一个例子表明,声波可以在双甘杆期间干净地分开。

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