Maximum a posteriori spectral estimation with source log-spectral priors for multichannel speech enhancement

Abstract

When speech signals are captured in real acoustical environments, the captured signals are distorted by certain types of interference, such as ambient noise, reverberation, and extraneous speakers’ utterances. There are two important approaches to speech enhancement that reduce such interference in the captured signals. One approach is based on the spatial features of the signals, such as direction of arrival and acoustic transfer functions, and enhances speech using multichannel audio signal processing. The other approach is based on speech spectral models that represent the probability density function of the speech spectra, and it enhances speech by distinguishing between speech and noise based on the spectral models. In this chapter, we propose a new approach that integrates the above two approaches. The proposed approach uses the spatial and spectral features of signals in a complementary manner to achieve reliable and accurate speech enhancement. The approach can be applied to various speech enhancement problems, including denoising, dereverberation, and blind source separation (BSS).In particular, in this chapter, we focus on applying the approach to BSS. We show experimentally that the proposed integration can improve the performance of BSS compared with a conventional approach.