Multi-channel speech coding and enhancement is an indispensable technology in speech communication. In order to verify the effectiveness of multi-channel speech coding and enhancement methods in the research and development, a microphone array speech simulator (MASS) used in room acoustic environment is proposed. The proposed MASS is the improvement and extension of the existing multi-channel speech simulator. It aims to simulate clean speech, noisy speech, clean speech with reverberation, noisy speech with reverberation, and noise signals by microphone array used for multi-channel coding and enhancement of speech signal in room acoustic environment. The experimental results of the multi-channel speech coding and enhancement prove that the MASS could well simulate the signals used in real room acoustic environment and can be applied to the research of the related fields.

At present, the technology of using microphone arrays for speech enhancement has been widely concerned, and the enhancement effect is excellent. The widely used Generalized Sidelobe Canceller (GSC) method can achieve good noise reduction for noisy speech in the additive noise acoustic environment, and achieve better intelligibility improvement. But there are also areas for improvement. In the lower branch of GSC, signal leakage caused by the estimation of the incident angle or the slight change of the position of the microphone array may cause the self-cancellation of target speech signal, thereby the severe speech distortion is caused. In this paper, the Generative Adversarial Network (GAN), which has broad application prospects in deep learning technology, replaces the lower branch of the traditional GSC structure, thus the self-cancellation of speech signals is avoided and improving the anti-error ability of the enhancement system is improved effectively.

In this paper, a multi-channel speech enhancement method with the minimum variance distortionless response (MVDR) beamforming method based on the time-frequency (T-F) masking is proposed. In this study, First, the logarithmic power spectrum (LPS) features of multichannel signals are used as input features to estimate a T-F mask of the reference microphone by the deep neural network (DNN) model. Then, the estimated mask is utilized to calculate speech covariance matrix that is used to estimate a steering vector for constructing the MVDR beamformer. The steering vector is estimated by the generalized eigenvalue decomposition (GEVD) method. Finally, the output speech of the beamformer is processed by the DNN-based IRM model. In order to prove the effectiveness of the proposed method, the perceptual evaluation of speech quality (PESQ) and the segment signal-to-noise ratio (SSNR) are employed. The experimental results show that the proposed method effectively increased the PESQ and SSNR.

Beamforming method can effectively remove background noise, even in the complex environment, so it is widely used in speech enhancement. We propose a novel Generalized Eigenvalue (GEV) beamforming with Blind Analytic Normalization (BAN) method. In this method, the GEV beamformer coefficients are constructed by estimating logarithmic power spectrum (LPS), which are used to filter multichannel speech signals, and post filter technology is used to further remove noise in the beamformed signals. Firstly, in order to estimate the LPS of speech signal in each channel, we use the data-driven method to train the deep neural network (DNN) model. Then, we use the well trained DNN model to estimate LPS, which is used to calculate the power spectral density (PSD) matrix of speech, and further obtain the coefficients of the GEV beamformer. Since the GEV beamformer will cause speech distortion, the BAN is employed to post-process the beamformed signal. Furthermore, single channel speech enhancement is used to reduce residual noise. Our experiment is conducted in 8-channel simulation data set. The experimental results show that, compared with some existing speech enhancement methods, the proposed method can effectively remove background noise and achieve better speech enhancement effect.