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

Daniele Giacobello; Tobias Lindstrøm Jensen

doi:10.1109/ICASSP.2018.8462560

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

Daniele Giacobello, Tobias Lindstrøm Jensen

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

10 Citations (Scopus)

387 Downloads (Pure)

Abstract

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.

Original language	English
Title of host publication	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Number of pages	5
Publisher	IEEE
Publication date	2018
Pages	446-450
Article number	8462560
ISBN (Print)	978-1-5386-4659-5
ISBN (Electronic)	978-1-5386-4658-8
DOIs	https://doi.org/10.1109/ICASSP.2018.8462560
Publication status	Published - 2018
Event	2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018 https://2018.ieeeicassp.org/

Conference

Conference	2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Country/Territory	Canada
City	Calgary
Period	15/04/2018 → 20/04/2018
Internet address	https://2018.ieeeicassp.org/

Series	I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN	1520-6149

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10.1109/ICASSP.2018.8462560

sparseLPdereverberationSubmitted manuscript, 220 KB

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Cite this

@inproceedings{d787ac0c84114f21bfaed63a76f8cc86,

title = "Speech Dereverberation Based on Convex Optimization Algorithms for Group Sparse Linear Prediction",

abstract = "In this paper, we consider methods for improving far-field speechrecognition using dereverberation based on sparse multi-channel linearprediction. In particular, we extend successful methods based on nonconvex iteratively reweighted least squares, that look for a sparsedesired 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 evaluatethe methods using large scale simulations by mimicking the application scenarios of interest. The experiments show that the proposedconvex formulations and regularization offer improvements over existing methods with added robustness and flexibility in fairly differentacoustic scenarios.",

author = "Daniele Giacobello and Jensen, {Tobias Lindstr{\o}m}",

year = "2018",

doi = "10.1109/ICASSP.2018.8462560",

language = "English",

isbn = "978-1-5386-4659-5",

series = "I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings",

publisher = "IEEE",

pages = "446--450",

booktitle = "IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018",

address = "United States",

note = "2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP ; Conference date: 15-04-2018 Through 20-04-2018",

url = "https://2018.ieeeicassp.org/",

}

Giacobello, D & Jensen, TL 2018, Speech Dereverberation Based on Convex Optimization Algorithms for Group Sparse Linear Prediction. in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018., 8462560, IEEE, I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings, pp. 446-450, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Alberta, Canada, 15/04/2018. https://doi.org/10.1109/ICASSP.2018.8462560

Speech Dereverberation Based on Convex Optimization Algorithms for Group Sparse Linear Prediction. / Giacobello, Daniele; Jensen, Tobias Lindstrøm.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018. IEEE, 2018. p. 446-450 8462560 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Speech Dereverberation Based on Convex Optimization Algorithms for Group Sparse Linear Prediction

AU - Giacobello, Daniele

AU - Jensen, Tobias Lindstrøm

PY - 2018

Y1 - 2018

N2 - In this paper, we consider methods for improving far-field speechrecognition using dereverberation based on sparse multi-channel linearprediction. In particular, we extend successful methods based on nonconvex iteratively reweighted least squares, that look for a sparsedesired 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 evaluatethe methods using large scale simulations by mimicking the application scenarios of interest. The experiments show that the proposedconvex formulations and regularization offer improvements over existing methods with added robustness and flexibility in fairly differentacoustic scenarios.

AB - In this paper, we consider methods for improving far-field speechrecognition using dereverberation based on sparse multi-channel linearprediction. In particular, we extend successful methods based on nonconvex iteratively reweighted least squares, that look for a sparsedesired 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 evaluatethe methods using large scale simulations by mimicking the application scenarios of interest. The experiments show that the proposedconvex formulations and regularization offer improvements over existing methods with added robustness and flexibility in fairly differentacoustic scenarios.

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DO - 10.1109/ICASSP.2018.8462560

M3 - Article in proceeding

SN - 978-1-5386-4659-5

T3 - I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings

SP - 446

EP - 450

BT - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018

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Y2 - 15 April 2018 through 20 April 2018

ER -

Giacobello D, Jensen TL. Speech Dereverberation Based on Convex Optimization Algorithms for Group Sparse Linear Prediction. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018. IEEE. 2018. p. 446-450. 8462560. (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings). doi: 10.1109/ICASSP.2018.8462560

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

Abstract

Conference

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RTC: Computational Oriented Real-time Convex Optimization in Signal Processing

Cite this

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

Abstract

Conference

Access to Document

AUB Link

Other files and links

Projects

RTC: Computational Oriented Real-time Convex Optimization in Signal Processing

Cite this