Speech Dereverberation Based on Convex Optimization Algorithms for Group Sparse Linear Prediction

Daniele Giacobello, Tobias Lindstrøm Jensen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

9 Citationer (Scopus)
278 Downloads (Pure)

Abstrakt

In this paper, we consider methods for improving far-field speech
recognition using dereverberation based on sparse multi-channel linear
prediction. In particular, we extend successful methods based on nonconvex iteratively reweighted least squares, that look for a sparse
desired speech signal in the short-term Fourier transform domain, by proposing sparsity promoting convex functions. Furthermore, we show how to improve performance by applying regularization into both the reweighted least squares and convex methods. We evaluate
the methods using large scale simulations by mimicking the application scenarios of interest. The experiments show that the proposed
convex formulations and regularization offer improvements over existing methods with added robustness and flexibility in fairly different
acoustic scenarios.
OriginalsprogEngelsk
TitelIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Antal sider5
ForlagIEEE
Publikationsdato2018
Sider446-450
Artikelnummer8462560
ISBN (Trykt)978-1-5386-4659-5
ISBN (Elektronisk)978-1-5386-4658-8
DOI
StatusUdgivet - 2018
Begivenhed2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, Canada
Varighed: 15 apr. 201820 apr. 2018
https://2018.ieeeicassp.org/

Konference

Konference2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Land/OmrådeCanada
ByCalgary
Periode15/04/201820/04/2018
Internetadresse
NavnI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Citationsformater