Imperial College London
Alternate

Patrick A. Naylor

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Speech & Acoustic Signal Processing
 
 
 
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Contact

 

+44 (0)20 7594 6235p.naylor Website

 
 
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Location

 

803Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Antonello:2017:10.1109/TASLP.2017.2730284,
author = {Antonello, N and De, Sena E and Moonen, M and Naylor, PA and Van, Waterschoot T},
doi = {10.1109/TASLP.2017.2730284},
journal = {IEEE/ACM Transactions on Audio Speech and Language Processing},
pages = {1929--1941},
title = {Room Impulse Response Interpolation Using a Sparse Spatio-Temporal Representation of the Sound Field},
url = {http://dx.doi.org/10.1109/TASLP.2017.2730284},
volume = {25},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - © 2017 IEEE. Room Impulse Responses (RIRs) are typically measured using a set of microphones and a loudspeaker. When RIRs spanning a large volume are needed, many microphone measurements must be used to spatially sample the sound field. In order to reduce the number of microphone measurements, RIRs can be spatially interpolated. In the present study, RIR interpolation is formulated as an inverse problem. This inverse problem relies on a particular acoustic model capable of representing the measurements. Two different acoustic models are compared: the plane wave decomposition model and a novel time-domain model, which consists of a collection of equivalent sources creating spherical waves. These acoustic models can both approximate any reverberant sound field created by a far-field sound source. In order to produce an accurate RIR interpolation, sparsity regularization is employed when solving the inverse problem. In particular, by combining different acoustic models with different sparsity promoting regularizations, spatial sparsity, spatio-spectral sparsity, and spatio-temporal sparsity are compared. The inverse problem is solved using a matrix-free large-scale optimization algorithm. Simulations show that the best RIR interpolation is obtained when combining the novel time-domain acoustic model with the spatio-temporal sparsity regularization, outperforming the results of the plane wave decomposition model even when far fewer microphone measurements are available.
AU  - Antonello,N
AU  - De,Sena E
AU  - Moonen,M
AU  - Naylor,PA
AU  - Van,Waterschoot T
DO  - 10.1109/TASLP.2017.2730284
EP  - 1941
PY  - 2017///
SN  - 2329-9290
SP  - 1929
TI  - Room Impulse Response Interpolation Using a Sparse Spatio-Temporal Representation of the Sound Field
T2  - IEEE/ACM Transactions on Audio Speech and Language Processing
UR  - http://dx.doi.org/10.1109/TASLP.2017.2730284
VL  - 25
ER  -
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