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Doctoral Thesis
DOI
https://doi.org/10.11606/T.3.2012.tde-26032012-115753
Document
Author
Full name
Flávio Protásio Ribeiro
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2012
Supervisor
Committee
Nascimento, Vítor Heloiz (President)
Apolinário Junior, José Antonio
Burt, Phillip Mark Seymour
Panazio, Cristiano Magalhães
Zakharov, Yuriy
Title in Portuguese
Arrays de microfones para medida de campos acústicos.
Keywords in Portuguese
Aproximação de Kronecker
Array processing
Imagens acústicas
Mínimos quadrados regularizados
Reconstrução esparsa
Transformadas rápidas
Abstract in Portuguese
Imageamento acústico é um problema computacionalmente caro e mal-condicionado, que envolve estimar distribuições de fontes com grandes arranjos de microfones. O método clássico para imageamento acústico utiliza beamforming, e produz a distribuição de fontes de interesse convoluída com a função de espalhamento do arranjo. Esta convolução borra a imagem ideal, significativamente diminuindo sua resolução. Convoluções podem ser evitadas com técnicas de ajuste de covariância, que produzem estimativas de alta resolução. Porém, estas têm sido evitadas devido ao seu alto custo computacional. Nesta tese, admitimos um arranjo bidimensional com geometria separável, e desenvolvemos transformadas rápidas para acelerar imagens acústicas em várias ordens de grandeza. Estas transformadas são genéricas, e podem ser aplicadas para acelerar beamforming, algoritmos de deconvolução e métodos de mínimos quadrados regularizados. Assim, obtemos imagens de alta resolução com algoritmos estado-da-arte, mantendo baixo custo computacional. Mostramos que arranjos separáveis produzem estimativas competitivas com as de geometrias espirais logaritmicas, mas com enormes vantagens computacionais. Finalmente, mostramos como estender este método para incorporar calibração, um modelo para propagação em campo próximo e superfícies focais arbitrárias, abrindo novas possibilidades para imagens acústicas.
Title in English
Microphone arrays for acoustic field measurements.
Keywords in English
Acoustic imaging
Array processing
Fast transform
Kronecker approximation
Regularized least squares
Sparse reconstruction
Abstract in English
Acoustic imaging is a computationally intensive and ill-conditioned inverse problem, which involves estimating high resolution source distributions with large microphone arrays. The classical method for acoustic imaging consists of beamforming, and produces the source distribution of interest convolved with the array point spread function. This convolution smears the image of interest, significantly reducing its effective resolution. Convolutions can be avoided with covariance fitting methods, which have been known to produce robust high-resolution estimates. However, these have been avoided due to prohibitive computational costs. In this thesis, we assume a 2D separable array geometry, and develop fast transforms to accelerate acoustic imaging by several orders of magnitude with respect to previous methods. These transforms are very generic, and can be applied to accelerate beamforming, deconvolution algorithms and regularized least-squares solvers. Thus, one can obtain high-resolution images with state-of-the-art algorithms, while maintaining low computational cost. We show that separable arrays deliver accuracy competitive with multi-arm spiral geometries, while producing huge computational benefits. Finally, we show how to extend this approach with array calibration, a near-field propagation model and arbitrary focal surfaces, opening new and exciting possibilities for acoustic imaging.
 
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Publishing Date
2012-04-11
 
WARNING: The material described below relates to works resulting from this thesis or dissertation. The contents of these works are the author's responsibility.
  • RIBEIRO, F. P., and NASCIMENTO, V. H. Fast transforms for acoustic imaging - Part I: Theory [doi:10.1109/TIP.2011.2118220]. IEEE Transactions on Image Processing [online], 2011, vol. 20, p. 2229-2240.
  • RIBEIRO, F. P., and NASCIMENTO, V. H. Fast transforms for acoustic imaging - Part II: Applications [doi:10.1109/TIP.2011.2118219]. IEEE Transactions on Image Processing [online], 2011, vol. 20, p. 2241-2247.
  • Masiero, B. S., RIBEIRO, F. P., and NASCIMENTO, V. H. Transducer Placement Strategy for Active Noise Control of Power Transformers. In 34. Deutschen Jahrestagung für Akustik DAGA 2008, Dresden, 2008. Fortschritte der Akustik: Plenarvorträge und Fachbeiträge der 34. Deutschen Jahrestagung für Akustik DAGA 2008.Dresden : Deutsche Gesellschaft für Akustik e.V., 2008.
  • RIBEIRO, F. P., and NASCIMENTO, V. H. A fast transform for acoustic imaging with separable arrays. In 2011 International Conference on Acoustics, Speech and Signal Processing, Praga, República Tcheca, 2011. Proceedings of the 2011 International Conference on Acoustics, Speech and Signal Processing., 2011.
  • RIBEIRO, F. P., and NASCIMENTO, V. H. A robust and computationally efficient method for tonal active noise control using a simplified secondary path model [doi:10.1109/ICASSP.2008.4517617]. In IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, 2008. Proceedings, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing., 2008.
  • RIBEIRO, F. P., and NASCIMENTO, V. H. Computationally efficient regularized acoustic imaging. In 2011 International Conference on Acoustics, Speech and Signal Processing, Praga, República Tcheca, 2011. Proceedings of the 2011 International Conference on Acoustics, Speech and Signal Processing., 2011.
  • RIBEIRO, F. P., and NASCIMENTO, V. H. Fast Near-field Acoustic Imaging with Separable Arrays [doi:10.1109/SSP.2011.5967723]. In 2011 IEEE Workshop on Statistical Signal Processing, Nice, 2011. Proceedings, 2011 IEEE Workshop on Statistical Signal Processing., 2011.
  • RIBEIRO, F. P., FLORENCIO, D., and NASCIMENTO, V. H. Crowdsourcing Subjective Image Quality Evaluation. In 2011 IEEE International Conference on Image Processing, Bruxelas, Bélgica, 2011. Proceedings, 2011 IEEE International Conference on Image Processing., 2011.
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