Speech Enhancement Using Polynomial Eigenvalue Decomposition

Abstract

Speech enhancement is important for applications such as telecommunications, hearing aids, automatic speech recognition and voice-controlled system. The enhancement algorithms aim to reduce interfering noise while minimizing any speech distortion. In this work for speech enhancement, we propose to use polynomial matrices in order to exploit the spatial, spectral as well as temporal correlations between the speech signals received by the microphone array. Polynomial matrices provide the necessary mathematical framework in order to exploit constructively the spatial correlations within and between sensor pairs, as well as the spectral-temporal correlations of broadband signals, such as speech. Specifically, the polynomial eigenvalue decomposition (PEVD) decorrelates simultaneously in space, time and frequency. We then propose a PEVD-based speech enhancement algorithm. Simulations and informal listening examples have shown that our method achieves noise reduction without introducing artefacts into the enhanced signal for white, babble and factory noise conditions between -10 dB to 30 dB SNR.