Modulation-domain Kalman Filtering For Monaural Blind Speech Denoising And Dereverberation

Abstract

We describe a monaural speech enhancement algorithm based on modulation-domain Kalman filtering to blindly track the time-frequency log-magnitude spectra of speech and reverberation. We propose an adaptive algorithm that performs blind joint denoising and dereverberation, while accounting for the inter-frame speech dynamics, by estimating the posterior distribution of the speech log-magnitude spectrum given the log-magnitude spectrum of the noisy reverberant speech. The Kalman filter update step models the non-linear relations between the speech, noise and reverberation log-spectra. The Kalman filtering algorithm uses a signal model that takes into account the reverberation parameters of the reverberation time, \(T_\{60\}\), and the direct-to-reverberant energy ratio (DRR) and also estimates and tracks the \(T_\{60\}\) and the DRR in every frequency bin in order to improve the estimation of the speech log-magnitude spectrum. The Kalman filtering algorithm is tested and graphs that depi

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• arxiv keydionelis2018modulation

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