用于单麦克风语音增强的多帧算法（例如，多帧最小方差无失真响应（MFMVDR）滤波器）能够利用短时傅立叶变换（STFT）域中相邻时间帧之间的语音相关性。假设所需语音帧间相关矢量和噪声相关矩阵的准确估计值可用，则表明MFMVDR滤波器可显着降低噪声，同时几乎不引入任何语音失真。..

Deep Multi-Frame MVDR Filtering for Single-Microphone Speech Enhancement

Multi-frame algorithms for single-microphone speech enhancement, e.g., the multi-frame minimum variance distortionless response (MFMVDR) filter, are able to exploit speech correlation across adjacent time frames in the short-time Fourier transform (STFT) domain. Provided that accurate estimates of the required speech interframe correlation vector and the noise correlation matrix are available, it has been shown that the MFMVDR filter yields a substantial noise reduction while hardly introducing any speech distortion.Aiming at merging the speech enhancement potential of the MFMVDR filter and the estimation capability of temporal convolutional networks (TCNs), in this paper we propose to embed the MFMVDR filter within a deep learning framework. The TCNs are trained to map the noisy speech STFT coefficients to the required quantities by minimizing the scale-invariant signal-to-distortion ratio loss function at the MFMVDR filter output. Experimental results show that the proposed deep MFMVDR filter achieves a competitive speech enhancement performance on the Deep Noise Suppression Challenge dataset. In particular, the results show that estimating the parameters of an MFMVDR filter yields a higher performance in terms of PESQ and STOI than directly estimating the multi-frame filter or single-frame masks and than Conv-TasNet.