Mask-based MVDR Beamformer for Noisy Multisource Environments: Introduction of Time-varying Spatial Covariance Model

This paper proposes a method for designing a time-varying minimum variance distortionless response (MVDR) beamformer using time-frequency masks, with the aim of improving speech enhancement in noisy multi-speaker environments. A key to successful beamforming is to estimate accurately a time-varying spatial covariance matrix (SCM) for noise composed of both stationary diffuse noise and highly time-varying speech. For this purpose, we introduce a stochastic model that can represent the time-varying characteristics of the noise SCM, and derive a method for estimating a time-varying noise SCM based on the model. Experiments show that the proposed method can substantially improve the performance of the beamformer in terms of automatic speech recognition (ASR) accuracy and source-to-distortion ratio compared with a conventional time-invariant MVDR beamformer.