In this letter, we address the problem of simultaneous separation and dereverberation of overlapped speech recorded in reverberant conditions. The majority of state-of-the-art techniques are tailored for either of the two problems, which for the joint task leads to sub-optimum performance or solutions which involve subsequent, cascade processing. In contrast, we propose a jointly optimum approach in which we formulate a single optimization criterion that minimizes variance of undesired signal components at the output of a convolutional filter, weighted with the desired speech variance, subject to a constraint which allows to control the amount of distortions in the estimated speech signal. We then derive a closed-form solution of the proposed convolutional weighted parametric multichannel Wiener (CW-PMW) filter which integrates linear-prediction based dereverberation and speech-distortion weighted Wiener filtering in a jointly optimum manner. The results of experiments performed using measured and simulated data indicate superior performance of the proposed approach in comparison with state-of-the-art, which includes sub-optimum cascades of optimum filters for individual tasks, as well as the recently presented jointly optimum, weighted power minimization distortionless response (WPD) beamformer.

中文翻译：

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用于混响源分离的卷积加权参数多通道维纳滤波器


在这封信中，我们解决了在混响条件下录制的重叠语音的同时分离和去混响问题。大多数最先进的技术都是针对这两个问题中的任何一个而定制的，这对于联合任务会导致次优的性能或涉及后续级联处理的解决方案。相比之下，我们提出了一种联合优化方法，其中我们制定了一个单一的优化标准，该标准可以最大限度地减少卷积滤波器输出处不需要的信号分量的方差，并用所需的语音方差进行加权，并受到允许控制语音量的约束。估计语音信号的失真。然后，我们推导了所提出的卷积加权参数多通道维纳（CW-PMW）滤波器的封闭式解决方案，该滤波器以联合最佳方式集成了基于线性预测的去混响和语音失真加权维纳滤波。使用测量和模拟数据进行的实验结果表明，与最先进的方法相比，所提出的方法具有优越的性能，其中包括针对各个任务的最优滤波器的次优级联，以及最近提出的联合最优，加权功率最小化无失真响应（WPD）波束形成器。