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Recursive Spatial Covariance Estimation with Sparse Priors for Sound Field Interpolation

Sundstrom, David LU ; Lindstrom, Johan LU orcid and Jakobsson, Andreas LU orcid (2023) 22nd IEEE Statistical Signal Processing Workshop, SSP 2023 In IEEE Workshop on Statistical Signal Processing Proceedings 2023-July. p.517-521
Abstract

Recent advances have shown that sound fields can be accurately interpolated between microphone measurements when the spatial covariance matrix is known. This matrix may be estimated in various ways; one promising approach is to use a plane wave formulation with sparse priors, although this may require the use of a many microphones to suppress the noise. To overcome this, we introduce a time domain formulation exploiting multiple time samples, posing the problem as an identification problem of a recursively estimated sample covariance matrix. A computationally efficient method is proposed to solve the resulting identification problem. Using both numerical experiments and anechoic data, the proposed method is shown to yield preferable... (More)

Recent advances have shown that sound fields can be accurately interpolated between microphone measurements when the spatial covariance matrix is known. This matrix may be estimated in various ways; one promising approach is to use a plane wave formulation with sparse priors, although this may require the use of a many microphones to suppress the noise. To overcome this, we introduce a time domain formulation exploiting multiple time samples, posing the problem as an identification problem of a recursively estimated sample covariance matrix. A computationally efficient method is proposed to solve the resulting identification problem. Using both numerical experiments and anechoic data, the proposed method is shown to yield preferable performance as compared to current state of the art methods, notably for high frequencies sources and/or in cases when using few microphones.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
covariance matrix estimation, Gaussian process, maximum likelihood, recursive estimation, Sound field interpolation, sparse priors
host publication
Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
series title
IEEE Workshop on Statistical Signal Processing Proceedings
volume
2023-July
pages
5 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
22nd IEEE Statistical Signal Processing Workshop, SSP 2023
conference location
Hanoi, Viet Nam
conference dates
2023-07-02 - 2023-07-05
external identifiers
  • scopus:85168856568
ISBN
9781665452458
DOI
10.1109/SSP53291.2023.10208010
language
English
LU publication?
yes
id
c625cb17-fa9d-43c3-b94f-d3999b714f01
date added to LUP
2023-11-30 13:32:53
date last changed
2024-05-31 15:30:24
@inproceedings{c625cb17-fa9d-43c3-b94f-d3999b714f01,
  abstract     = {{<p>Recent advances have shown that sound fields can be accurately interpolated between microphone measurements when the spatial covariance matrix is known. This matrix may be estimated in various ways; one promising approach is to use a plane wave formulation with sparse priors, although this may require the use of a many microphones to suppress the noise. To overcome this, we introduce a time domain formulation exploiting multiple time samples, posing the problem as an identification problem of a recursively estimated sample covariance matrix. A computationally efficient method is proposed to solve the resulting identification problem. Using both numerical experiments and anechoic data, the proposed method is shown to yield preferable performance as compared to current state of the art methods, notably for high frequencies sources and/or in cases when using few microphones.</p>}},
  author       = {{Sundstrom, David and Lindstrom, Johan and Jakobsson, Andreas}},
  booktitle    = {{Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023}},
  isbn         = {{9781665452458}},
  keywords     = {{covariance matrix estimation; Gaussian process; maximum likelihood; recursive estimation; Sound field interpolation; sparse priors}},
  language     = {{eng}},
  pages        = {{517--521}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Workshop on Statistical Signal Processing Proceedings}},
  title        = {{Recursive Spatial Covariance Estimation with Sparse Priors for Sound Field Interpolation}},
  url          = {{http://dx.doi.org/10.1109/SSP53291.2023.10208010}},
  doi          = {{10.1109/SSP53291.2023.10208010}},
  volume       = {{2023-July}},
  year         = {{2023}},
}