Acoustic models for online blind source dekevemberation using sequential monte carlo methods

Christine Evers; James R. Hopgood; Judith Bell

doi:10.1109/ICASSP.2008.4518680

Acoustic models for online blind source dekevemberation using sequential monte carlo methods

Christine Evers, James R. Hopgood, Judith Bell

School of Engineering & Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Reverberation and noise cause significant deterioration of audio quality and intelligibility to signals recorded in acoustic environments. Noise is usually modeled as a common signal observed in the room and independent of room acoustics. However, this simplistic model cannot necessarily capture the effects of separate noise sources at different locations in the room. This paper proposes a noise model that considers distinct noise sources whose individual acoustic impulse responses are separated into source-sensor specific and common acoustical resonances. Further to noise, the signal is distorted by reverberation. Using parametric models of the system, recursive expressions of the filtering distribution can be derived. Based on these results, a sequential Monte Carlo approach for online dereverberation and enhancement is proposed. Simulation results for speech are presented to verify the effectiveness of the model and method. ©2008 IEEE.

Original language	English
Title of host publication	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages	4597-4600
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2008.4518680
Publication status	Published - 2008
Event	33rd IEEE International Conference on Acoustics, Speech and Signal Processing 2008 - Las Vegas, NV, USA, Las Vegas, United States Duration: 30 Mar 2008 → 4 Apr 2008

Conference

Conference	33rd IEEE International Conference on Acoustics, Speech and Signal Processing 2008
Abbreviated title	ICASSP 2008
Country/Territory	United States
City	Las Vegas
Period	30/03/08 → 4/04/08

Keywords

Acoustic signal processing
Monte Carlo
Speech dereverberation sequential estimation
Speech enhancement

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10.1109/ICASSP.2008.4518680

Cite this

@inproceedings{5adabff73214491ea7d709d8c6ceaa5a,

title = "Acoustic models for online blind source dekevemberation using sequential monte carlo methods",

abstract = "Reverberation and noise cause significant deterioration of audio quality and intelligibility to signals recorded in acoustic environments. Noise is usually modeled as a common signal observed in the room and independent of room acoustics. However, this simplistic model cannot necessarily capture the effects of separate noise sources at different locations in the room. This paper proposes a noise model that considers distinct noise sources whose individual acoustic impulse responses are separated into source-sensor specific and common acoustical resonances. Further to noise, the signal is distorted by reverberation. Using parametric models of the system, recursive expressions of the filtering distribution can be derived. Based on these results, a sequential Monte Carlo approach for online dereverberation and enhancement is proposed. Simulation results for speech are presented to verify the effectiveness of the model and method. {\textcopyright}2008 IEEE.",

keywords = "Acoustic signal processing, Monte Carlo, Speech dereverberation sequential estimation, Speech enhancement",

author = "Christine Evers and Hopgood, {James R.} and Judith Bell",

year = "2008",

doi = "10.1109/ICASSP.2008.4518680",

language = "English",

isbn = "1424414849",

pages = "4597--4600",

booktitle = "2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP",

note = "33rd IEEE International Conference on Acoustics, Speech and Signal Processing 2008, ICASSP 2008 ; Conference date: 30-03-2008 Through 04-04-2008",

}

Evers, C, Hopgood, JR & Bell, J 2008, Acoustic models for online blind source dekevemberation using sequential monte carlo methods. in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. pp. 4597-4600, 33rd IEEE International Conference on Acoustics, Speech and Signal Processing 2008, Las Vegas, United States, 30/03/08. https://doi.org/10.1109/ICASSP.2008.4518680

TY - GEN

T1 - Acoustic models for online blind source dekevemberation using sequential monte carlo methods

AU - Evers, Christine

AU - Hopgood, James R.

AU - Bell, Judith

PY - 2008

Y1 - 2008

N2 - Reverberation and noise cause significant deterioration of audio quality and intelligibility to signals recorded in acoustic environments. Noise is usually modeled as a common signal observed in the room and independent of room acoustics. However, this simplistic model cannot necessarily capture the effects of separate noise sources at different locations in the room. This paper proposes a noise model that considers distinct noise sources whose individual acoustic impulse responses are separated into source-sensor specific and common acoustical resonances. Further to noise, the signal is distorted by reverberation. Using parametric models of the system, recursive expressions of the filtering distribution can be derived. Based on these results, a sequential Monte Carlo approach for online dereverberation and enhancement is proposed. Simulation results for speech are presented to verify the effectiveness of the model and method. ©2008 IEEE.

AB - Reverberation and noise cause significant deterioration of audio quality and intelligibility to signals recorded in acoustic environments. Noise is usually modeled as a common signal observed in the room and independent of room acoustics. However, this simplistic model cannot necessarily capture the effects of separate noise sources at different locations in the room. This paper proposes a noise model that considers distinct noise sources whose individual acoustic impulse responses are separated into source-sensor specific and common acoustical resonances. Further to noise, the signal is distorted by reverberation. Using parametric models of the system, recursive expressions of the filtering distribution can be derived. Based on these results, a sequential Monte Carlo approach for online dereverberation and enhancement is proposed. Simulation results for speech are presented to verify the effectiveness of the model and method. ©2008 IEEE.

KW - Acoustic signal processing

KW - Monte Carlo

KW - Speech dereverberation sequential estimation

KW - Speech enhancement

UR - http://www.scopus.com/inward/record.url?scp=51449118246&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2008.4518680

DO - 10.1109/ICASSP.2008.4518680

M3 - Conference contribution

SN - 1424414849

SN - 9781424414840

SP - 4597

EP - 4600

BT - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP

T2 - 33rd IEEE International Conference on Acoustics, Speech and Signal Processing 2008

Y2 - 30 March 2008 through 4 April 2008

ER -

Acoustic models for online blind source dekevemberation using sequential monte carlo methods

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Keywords

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