Speech dereverberation performance of a polynomial-EVD subspace approach

Neo, Vincent W., Evers, Christine and Naylor, Patrick A. (2020) Speech dereverberation performance of a polynomial-EVD subspace approach. European Signal Processing Conference, , Amsterdam, Netherlands. 18 - 22 Jan 2021. 5 pp . (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

The degradation of speech arising from additive background noise and reverberation affects the performance of important speech applications such as telecommunications, hearing aids, voice-controlled systems and robot audition. In this work, we focus on dereverberation. It is shown that the parameterized polynomial matrix eigenvalue decomposition (PEVD)-based speech enhancement algorithm exploits the lack of correlation between speech and the late reflections to enhance the speech component associated with the direct path and early reflections. The algorithm’s performance is evaluated using simulations involving measured acoustic impulse responses and noise from the ACE corpus. The simulations and informal listening examples have indicated that the PEVD-based algorithm performs dereverberation over a range of SNRs without introducing any noticeable processing artefacts.

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neo2020 - Accepted Manuscript

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