Enhancement of Reverberant and Noisy Speech by Extending Its Coherence

We introduce a novel speech enhancement algorithm for removing reverberation and noise from recorded speech data. Our approach centers around using a single-channel minimum mean-square error log-spectral amplitude (MMSELSA) estimator, which applies gain coefficients in a timefrequency domain to suppress noise and reverberation. The main contribution of this paper is that the enhancement is done in a time-frequency domain that is coherent with speech signals over longer analysis durations than the short-time Fourier transform (STFT) domain. This extended coherence is gained by using a linear model of fundamental frequency variation over the analysis frame. In the multichannel case, we preprocess the data with either a minimum variance distortionless response (MVDR) beamformer, or a delay-and-sum beamformer (DSB). We evaluate our algorithm on the REVERB challenge dataset. Compared to the same processing done in the STFT domain, our approach achieves significant improvement on the REVERB challenge objective metrics, and according to informal listening tests, results in fewer artifacts in the enhanced speech.