Single Channel Blind Dereverberation of Speech Signals

TL;DR

This paper explores various non-negative matrix factor deconvolution techniques for single-channel speech dereverberation, proposing novel methods and comparing their effectiveness using objective speech quality metrics.

Contribution

It introduces a new NMFD-based approach for dereverberation and extends existing methods with temporal modeling, providing a comparative analysis of techniques on standard datasets.

Findings

The proposed NMFD-based method shows improved PESQ scores.

Temporal extensions enhance dereverberation performance.

Exact results remain inconsistent despite quantitative improvements.

Abstract

Dereverberation of recorded speech signals is one of the most pertinent problems in speech processing. In the present work, the objective is to understand and implement dereverberation techniques that aim at enhancing the magnitude spectrogram of reverberant speech signals to remove the reverberant effects introduced. An approach to estimate a clean speech spectrogram from the reverberant speech spectrogram is proposed. This is achieved through non-negative matrix factor deconvolution(NMFD). Further, this approach is extended using the NMF representation for speech magnitude spectrograms. To exploit temporal dependencies, a convolutive NMF-based representation and a frame-stacked model are incorporated into the NMFD framework for speech. A novel approach for dereverberation by applying NMFD to the activation matrix of the reverberated magnitude spectrogram is also proposed. Finally, a comparative analysis of the performance of the listed techniques, using sentence recordings from the TIMIT database and recorded room impulse responses from the Reverb 2014 challenge, is presented based on two key objective measures - PESQ and Cepstral Distortion.\\ Although we were qualitatively able to verify the claims made in literature regarding these techniques, exact results could not be matched. The novel approach, as it is suggested, provides improvement in quantitative metrics, but is not consistent