Multi-Channel Speech Source Separation and Dereverberation With Sequential Integration of Determined and Underdetermined Models
Multi-Channel Speech Source Separation and Dereverberation With Sequential Integration of Determined and Underdetermined Models
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DOI：
10.1109/icassp40776.2020.9054766
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发表时间：
2020-05
期刊：
影响因子：
--
通讯作者：
M. Togami
中科院分区：
文献类型：
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作者：
M. Togami
本文提出了一种联合多声道语音源分离和去混响方法，其中多个语音源和滞后混响以无监督的方式分离。该方法联合优化了基于自回归(AR)模型的语音去混响和基于时变多通道维纳滤波(MWF)的语音源分离。为了提高分离和去混响性能，克服频间置换问题，该方法采用了一种序贯参数优化策略。首先，基于确定的模型对参数进行更新，并基于非负矩阵分解求解置换问题。所确定的模型仅通过AR模型减少混响，残留混响。该方法基于已确定模型的参数可以转换为欠确定模型的参数的特点，将剩余混响视为一个附加源，并在欠确定模型的基础上用转换后的参数来减少剩余混响。在欠定模型的基础上，我们进一步提出了参数的额外更新。实验结果表明，该方法优于仅基于已确定模型的传统方法，基于欠定模型的附加参数更新方法是有效的。
In this paper, we propose a joint multi-channel speech source separation and dereverberation method in which multiple speech sources and late reverberation are separated in an unsupervised manner. The proposed method jointly optimizes an auto-regressive (AR) model based speech dereverberation and a time-varying multichannel Wiener filtering (MWF) based speech source separation. So as to increase separation and dereverberation performance and to overcome the inter-frequency permutation problem, the proposed method adopts a sequential parameter optimization strategy. At first, the parameter is updated based on a determined model, and the permutation problem can be solved based on the non-negative matrix factorization. The determined model reduces reverberation by only the AR model and residual reverberation remains. Inspired by the fact that a parameter of a determined model can be converted into a parameter of a underdetermined model, the proposed method regards residual reverberation as an additional source and reduces residual reverberation with the converted parameter based on the underdetermined model. We further propose additional update of the parameter based on the underdetermined model. Experimental results show that the proposed method outperforms the conventional method based on only the determined model and the proposed additional parameter update based on the underdetermined model is effective.