An Expectation-Maximization Algorithm for Multimicrophone Speech Dereverberation and Noise Reduction With Coherence Matrix Estimation
An Expectation-Maximization Algorithm for Multimicrophone Speech Dereverberation and Noise Reduction With Coherence Matrix Estimation
复制标题
一种基于相干矩阵估计的多麦克风语音去混响和降噪的期望最大化算法
DOI：
10.1109/taslp.2016.2553457
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Emanuël Habets
中科院分区：
文献类型：
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作者：
Ofer Schwartz;S. Gannot;Emanuël Habets
在语音通信系统中，麦克风信号会因混响和环境噪声而降低。混响语音可以分为两个分量，即由直接路径和一些早期反射组成的早期语音分量和由所有晚期反射组成的晚期混响分量。本文提出了一种同时抑制早期反射、后期混响和环境噪声的新颖算法。期望最大化（EM）算法用于估计早期语音分量和晚期混响分量的信号和空间参数。结果，在 E 步骤中估计了早期语音分量的空间滤波版本。在 EM 算法的 M 步中估计无回声语音的功率谱密度 (PSD)、相对早期传递函数以及后期混响的 PSD 矩阵。该算法使用我们声学实验室记录的真实房间脉冲响应进行评估，混响时间设置为 0.36 秒和 0.61 秒，以及多个信噪比级别。结果表明，该算法取得了显着的改进，并且优于基线单通道和多通道混响算法以及最先进的多通道混响算法。
In speech communication systems, the microphone signals are degraded by reverberation and ambient noise. The reverberant speech can be separated into two components, namely, an early speech component that consists of the direct path and some early reflections and a late reverberant component that consists of all late reflections. In this paper, a novel algorithm to simultaneously suppress early reflections, late reverberation, and ambient noise is presented. The expectation-maximization (EM) algorithm is used to estimate the signals and spatial parameters of the early speech component and the late reverberation components. As a result, a spatially filtered version of the early speech component is estimated in the E-step. The power spectral density (PSD) of the anechoic speech, the relative early transfer functions, and the PSD matrix of the late reverberation are estimated in the M-step of the EM algorithm. The algorithm is evaluated using real room impulse response recorded in our acoustic lab with a reverberation time set to 0.36 s and 0.61 s and several signal-to-noise ratio levels. It is shown that significant improvement is obtained and that the proposed algorithm outperforms baseline single-channel and multichannel dereverberation algorithms, as well as a state-of-the-art multichannel dereverberation algorithm.