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Sparse parametric modeling of the early part of acoustic impulse responses

Sparse parametric modeling of the early part of acoustic impulse responses
Sparse parametric modeling of the early part of acoustic impulse responses

Acoustic channels are typically described by their Acoustic Impulse Response (AIR) as a Moving Average (MA) process. Such AIRs are often considered in terms of their early and late parts, describing discrete reflections and the diffuse reverberation tail respectively. We propose an approach for constructing a sparse parametric model for the early part. The model aims at reducing the number of parameters needed to represent it and subsequently reconstruct from the representation the MA coefficients that describe it. It consists of a representation of the reflections arriving at the receiver as delayed copies of an excitation signal. The Time-Of-Arrivals of reflections are not restricted to integer sample instances and a dynamically estimated model for the excitation sound is used. We also present a corresponding parameter estimation method, which is based on regularized-regression and nonlinear optimization. The proposed method also serves as an analysis tool, since estimated parameters can be used for the estimation of room geometry, the mixing time and other channel properties. Experiments involving simulated and measured AIRs are presented, in which the AIR coefficient reconstruction-error energy does not exceed 11.4% of the energy of the original AIR coefficients. The results also indicate dimensionality reduction figures exceeding 90% when compared to a MA process representation.

Acoustic Environments, Reflection TOA Estimation, Reverberation, Sparse Modeling
678-682
Institute of Electrical and Electronics Engineers Inc.
Papayiannis, Constantinos
eb7beecd-5217-4171-8c45-ce853dbd03f5
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b
Papayiannis, Constantinos
eb7beecd-5217-4171-8c45-ce853dbd03f5
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b

Papayiannis, Constantinos, Evers, Christine and Naylor, Patrick A. (2017) Sparse parametric modeling of the early part of acoustic impulse responses. In European Signal Processing Conference (EUSIPCO). vol. 2017-January, Institute of Electrical and Electronics Engineers Inc. pp. 678-682 . ().

Record type: Conference or Workshop Item (Paper)

Abstract

Acoustic channels are typically described by their Acoustic Impulse Response (AIR) as a Moving Average (MA) process. Such AIRs are often considered in terms of their early and late parts, describing discrete reflections and the diffuse reverberation tail respectively. We propose an approach for constructing a sparse parametric model for the early part. The model aims at reducing the number of parameters needed to represent it and subsequently reconstruct from the representation the MA coefficients that describe it. It consists of a representation of the reflections arriving at the receiver as delayed copies of an excitation signal. The Time-Of-Arrivals of reflections are not restricted to integer sample instances and a dynamically estimated model for the excitation sound is used. We also present a corresponding parameter estimation method, which is based on regularized-regression and nonlinear optimization. The proposed method also serves as an analysis tool, since estimated parameters can be used for the estimation of room geometry, the mixing time and other channel properties. Experiments involving simulated and measured AIRs are presented, in which the AIR coefficient reconstruction-error energy does not exceed 11.4% of the energy of the original AIR coefficients. The results also indicate dimensionality reduction figures exceeding 90% when compared to a MA process representation.

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More information

Published date: 23 October 2017
Venue - Dates: 25th European Signal Processing Conference, EUSIPCO 2017, , Kos, Greece, 2017-08-27 - 2017-09-01
Keywords: Acoustic Environments, Reflection TOA Estimation, Reverberation, Sparse Modeling

Identifiers

Local EPrints ID: 445081
URI: http://eprints.soton.ac.uk/id/eprint/445081
DOI:
PURE UUID: ec495d28-aabb-496b-802f-e677444822fb
ORCID for Christine Evers:

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Date deposited: 19 Nov 2020 17:30
Last modified: 19 Nov 2020 17:30

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Contributors

Author: Constantinos Papayiannis
Author: Christine Evers
Author: Patrick A. Naylor

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