Recursive Spatial Covariance Estimation with Sparse Priors for Sound Field Interpolation
(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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- author
- Sundstrom, David LU ; Lindstrom, Johan LU and Jakobsson, Andreas LU
- organization
-
- Mathematical Statistics
- LTH Profile Area: Aerosols
- eSSENCE: The e-Science Collaboration
- LU Profile Area: Natural and Artificial Cognition
- LTH Profile Area: AI and Digitalization
- LTH Profile Area: Engineering Health
- Biomedical Modelling and Computation (research group)
- Statistical Signal Processing Group (research group)
- publishing date
- 2023
- 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}}, }