Sparse Modeling of The Early Part of Noisy Room Impulse Responses with Sparse Bayesian Learning

Maozhong Fu; Jesper Rindom Jensen; Yuhan Li; Mads Græsbøll Christensen

doi:10.1109/ICASSP43922.2022.9746069

Sparse Modeling of The Early Part of Noisy Room Impulse Responses with Sparse Bayesian Learning

Maozhong Fu, Jesper Rindom Jensen, Yuhan Li, Mads Græsbøll Christensen

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

2 Citationer (Scopus)

Abstract

A model of a room impulse response (RIR) is useful for a wide range of applications. Typically, the early part of a RIR is sparse, and its sparse structure allows for accurate and simple modeling of the RIR. The existing p(0 < p ≤ 1)-norm-based methods suffer from the sensitivity to the user-selected regularization parameters or a high computational burden. In this work, we propose to reconstruct the sparse model for the early part of RIRs with sparse Bayesian learning (SBL). Under the framework of SBL, the proposed method can adaptively learn the optimal hyper-parameters from data at a low computational cost. Experiment results show that the proposed method has advantages in terms of noise robustness, reconstruction sparsity, and computational efficiency compared to the existing methods.

Originalsprog	Engelsk
Titel	2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
Antal sider	5
Forlag	IEEE
Publikationsdato	2022
Sider	586-590
Artikelnummer	9746069
ISBN (Trykt)	978-1-6654-0541-6
ISBN (Elektronisk)	978-1-6654-0540-9
DOI	https://doi.org/10.1109/ICASSP43922.2022.9746069
Status	Udgivet - 2022
Begivenhed	47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore Varighed: 23 maj 2022 → 27 maj 2022

Konference

Konference	47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Land/Område	Singapore
By	Virtual, Online
Periode	23/05/2022 → 27/05/2022
Sponsor	Chinese and Oriental Languages Information Processing Society (COLPIS), Singapore Exhibition and Convention Bureau, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), The Institute of Electrical and Electronics Engineers Signal Processing Society

Navn	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Vol/bind	2022-May
ISSN	1520-6149

Bibliografisk note

Funding Information:
This work was supported by China Scholarship Council.

Publisher Copyright:
© 2022 IEEE

Adgang til dokumentet

10.1109/ICASSP43922.2022.9746069

AUB Link

Søg efter materialet i Aalborg Universitetsbiblioteks søgemaskine

Andre filer og links

Link to publication in Scopus

Citationsformater

Fu, Maozhong ; Jensen, Jesper Rindom ; Li, Yuhan et al. / Sparse Modeling of The Early Part of Noisy Room Impulse Responses with Sparse Bayesian Learning. 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. IEEE, 2022. s. 586-590 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Bind 2022-May).

@inproceedings{676941b6bda243f7abf205d2f5e0dde7,

title = "Sparse Modeling of The Early Part of Noisy Room Impulse Responses with Sparse Bayesian Learning",

abstract = "A model of a room impulse response (RIR) is useful for a wide range of applications. Typically, the early part of a RIR is sparse, and its sparse structure allows for accurate and simple modeling of the RIR. The existing p(0 < p ≤ 1)-norm-based methods suffer from the sensitivity to the user-selected regularization parameters or a high computational burden. In this work, we propose to reconstruct the sparse model for the early part of RIRs with sparse Bayesian learning (SBL). Under the framework of SBL, the proposed method can adaptively learn the optimal hyper-parameters from data at a low computational cost. Experiment results show that the proposed method has advantages in terms of noise robustness, reconstruction sparsity, and computational efficiency compared to the existing methods.",

keywords = "Room impulse response, sparse Bayesian learning, sparse modeling",

author = "Maozhong Fu and Jensen, {Jesper Rindom} and Yuhan Li and Christensen, {Mads Gr{\ae}sb{\o}ll}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE; 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 ; Conference date: 23-05-2022 Through 27-05-2022",

year = "2022",

doi = "10.1109/ICASSP43922.2022.9746069",

language = "English",

isbn = "978-1-6654-0541-6",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "IEEE",

pages = "586--590",

booktitle = "2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings",

address = "United States",

}

Fu, M, Jensen, JR, Li, Y & Christensen, MG 2022, Sparse Modeling of The Early Part of Noisy Room Impulse Responses with Sparse Bayesian Learning. i 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings., 9746069, IEEE, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, bind 2022-May, s. 586-590, 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, Virtual, Online, Singapore, 23/05/2022. https://doi.org/10.1109/ICASSP43922.2022.9746069

Sparse Modeling of The Early Part of Noisy Room Impulse Responses with Sparse Bayesian Learning. / Fu, Maozhong; Jensen, Jesper Rindom; Li, Yuhan et al.
2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. IEEE, 2022. s. 586-590 9746069 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Bind 2022-May).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Sparse Modeling of The Early Part of Noisy Room Impulse Responses with Sparse Bayesian Learning

AU - Fu, Maozhong

AU - Jensen, Jesper Rindom

AU - Li, Yuhan

AU - Christensen, Mads Græsbøll

PY - 2022

Y1 - 2022

N2 - A model of a room impulse response (RIR) is useful for a wide range of applications. Typically, the early part of a RIR is sparse, and its sparse structure allows for accurate and simple modeling of the RIR. The existing p(0 < p ≤ 1)-norm-based methods suffer from the sensitivity to the user-selected regularization parameters or a high computational burden. In this work, we propose to reconstruct the sparse model for the early part of RIRs with sparse Bayesian learning (SBL). Under the framework of SBL, the proposed method can adaptively learn the optimal hyper-parameters from data at a low computational cost. Experiment results show that the proposed method has advantages in terms of noise robustness, reconstruction sparsity, and computational efficiency compared to the existing methods.

AB - A model of a room impulse response (RIR) is useful for a wide range of applications. Typically, the early part of a RIR is sparse, and its sparse structure allows for accurate and simple modeling of the RIR. The existing p(0 < p ≤ 1)-norm-based methods suffer from the sensitivity to the user-selected regularization parameters or a high computational burden. In this work, we propose to reconstruct the sparse model for the early part of RIRs with sparse Bayesian learning (SBL). Under the framework of SBL, the proposed method can adaptively learn the optimal hyper-parameters from data at a low computational cost. Experiment results show that the proposed method has advantages in terms of noise robustness, reconstruction sparsity, and computational efficiency compared to the existing methods.

KW - Room impulse response

KW - sparse Bayesian learning

KW - sparse modeling

UR - http://www.scopus.com/inward/record.url?scp=85131267185&partnerID=8YFLogxK

U2 - 10.1109/ICASSP43922.2022.9746069

DO - 10.1109/ICASSP43922.2022.9746069

M3 - Article in proceeding

AN - SCOPUS:85131267185

SN - 978-1-6654-0541-6

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 586

EP - 590

BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings

PB - IEEE

T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022

Y2 - 23 May 2022 through 27 May 2022

ER -

Fu M, Jensen JR, Li Y, Christensen MG. Sparse Modeling of The Early Part of Noisy Room Impulse Responses with Sparse Bayesian Learning. I 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. IEEE. 2022. s. 586-590. 9746069. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Bind 2022-May). doi: 10.1109/ICASSP43922.2022.9746069

Sparse Modeling of The Early Part of Noisy Room Impulse Responses with Sparse Bayesian Learning

Abstract

Konference

Bibliografisk note

Adgang til dokumentet

AUB Link

Andre filer og links

Fingeraftryk

Citationsformater