论文标题
通过融合MVDR和MCLP的有效覆盖算法
An Effective Dereverberation Algorithm by Fusing MVDR and MCLP
论文作者
论文摘要
在混响的情况下，人机互动的经验将变得更糟。为了解决这个问题，已经出现了许多用于覆盖的方法。目前，如何基于多通道线性预测（MCLP）在现有的替代方法中更新卡尔曼过滤器的参数是一项具有挑战性的任务，尤其是对目标语音的准确功率谱密度（PSD）估计。在本文中，最小差异无失真响应（MVDR）光束器和MCLP有效地融合在静脉结束中，其中MCLP中使用了用于Kalman滤波器的目标语音PSD。为了构建MVDR波束形式，晚期混响的PSD和噪声的PSD由基于阻止的PSD估计器同时估计。因此，可以通过从观察信号的PSD中减去噪声的PSD来获得用于卡尔曼滤波器的目标语音的PSD。与参考方法相比，提出的方法显示出出色的性能。
In the scenario with reverberation, the experience of human-machine interaction will become worse. In order to solve this problem, many methods for the dereverberation have emerged. At present, how to update the parameters of the Kalman filter in the existing dereverberation methods based on multichannel linear prediction (MCLP) is a challenging task, especially, accurate power spectral density (PSD) estimation of target speech. In this paper, minimum variance distortionless response (MVDR) beamformer and MCLP are effectively fused in the dereverberation, where the PSD of target speech used for Kalman filter is modified in the MCLP. In order to construct a MVDR beamformer, the PSD of late reverberation and the PSD of the noise are estimated simultaneously by the blocking-based PSD estimator. Thus, the PSD of target speech used for Kalman filter can be obtained by subtracting the PSD of late reverberation and the PSD of the noise from the PSD of observation signal. Compared to the reference methods, the proposed method shows an outstanding performance.
