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, T60, and the direct-to-reverberant energy ratio (DRR) and also estimates and tracks the T60 and the DRR in every frequency bin to improve the estimation of the speech log-spectrum. The proposed algorithm is evaluated in terms of speech quality, speech intelligibility and dereverberation performance for a range of reverberation parameters and reverberant speech to noise ratios, in different noises, and is also compared to competing denoising and dereverberation techniques. Experimental results using noisy reverberant speech demonstrate the effectiveness of the enhancement algorithm.

Phase-Aware Single-Channel Speech Enhancement With Modulation-Domain Kalman Filtering

“…Non-linear adaptive modulation-domain Kalman filtering algorithms can be used for speech enhancement, i.e. noise suppression and dereverberation, as in [1], [2], [3], [4] and [5]. Modulation-domain Kalman filtering can be applied for both noise and late reverberation suppression; in [2], [1], [3] and [4], various model-based speech enhancement algorithms that perform modulation-domain Kalman filtering are designed, implemented and tested.…”

“…The model-based speech enhancement algorithms in [1] and in [2], which estimates and tracks the clean speech phase, solve the problem of monaural speech enhancement using modulation-domain Kalman filtering, which refers to imposing temporal constraints on a spectral time-frequency domain. Three possible domains are the amplitude spectral domain, the power spectral domain and the log-spectral domain.…”

“…According to the literature, the reverberation time, T 60 , is independent of the source to microphone configuration; in contrast to the RIR, the T 60 measured in the diffuse sound field is independent of the source to microphone configuration. This is important for blindly estimating T 60 from noisy reverberant speech [1].…”

“…Speech recordings are degraded by noise and reverberation when captured using a near-field or far-field distant microphone within a confined acoustic space. Noise and reverberation have a detrimental impact on speech quality and speech intelligibility [1] [12]. Providing robustness to speech systems still remains a challenge due to noise and reverberation.…”

“…Reverberation can be quite long with a reverberation time, T 60 , of more than 900 ms. Noise is uncorrelated with speech [1], early reflections are strongly correlated with speech and late reverberation is uncorrelated with speech. Early reverberation is not perceived as separate sound sources and is correlated with clean speech [12].…”