Title: Multi-channel linear prediction-based speech dereverberation with low-rank power spectrogram approximation
Authors: Jukic, Ante
Mohammadiha, Nasser
van Waterschoot, Toon
Gerkmann, Timo
Doclo, Simon
Issue Date: Apr-2015
Publisher: IEEE
Host Document: Proceedings of the 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing pages:96-100
Conference: ICASSP 2015 location:Brisbane, Australia date:19-24 April 2015
Abstract: In many acoustic conditions the recorded speech signals may be severely affected by reverberation, leading to a reduced speech quality and intelligibility. In this paper we focus on a blind speech dereverberation method based on multi-channel linear prediction (MCLP) in the short-time Fourier transform domain, which is typically performed in each frequency bin independently without taking into account the spectral structure of the speech signal. Since it is widely accepted that a speech spectrogram can be well approximated with a low-rank matrix, e.g., using a spectral dictionary, in this paper we propose to incorporate a low-rank matrix approximation of the speech spectrogram into the MCLP-based speech dereverberation. The low-rank approximation is obtained using nonnegative matrix factorization with Itakura-Saito divergence. Experimental results for several measured acoustic systems show that incorporating a low-rank approximation improves the dereverberation performance in terms of instrumental speech quality measures.
ISBN: 9781467369978
Publication status: published
KU Leuven publication type: IC
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Electrical Engineering (ESAT) TC, Technology Campus Geel
Technologiecluster ESAT Elektrotechnische Engineering

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