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Spatial reconstruction of the sound field in a room in the modal frequency range using Bayesian inference
Spatial characterization of the sound field in a room is a challenging task, as it usually requires a large number of measurement points. This paper presents a probabilistic approach for sound field reconstruction in the modal frequency range for small and medium-sized rooms based on Bayesian inference. A plane wave expansion model is used to decompose the sound field in the examined domain. The posterior distribution for the amplitude of each plane wave is inferred based on a uniform prior distribution with limits based on the maximum sound pressure observed in the measurements. Two different application cases are studied, namely a numerically computed sound field in a non-rectangular two-dimensional (2D) domain and a measured sound field in a horizontal evaluation area of a lightly damped room. The proposed reconstruction method provides an accurate reconstruction for both examined cases. Further, the results of Bayesian inference are compared to the reconstruction with a deterministic compressive sensing framework. The most significant advantage of the Bayesian method over deterministic reconstruction approaches is that it provides a probability distribution of the sound pressure at every reconstruction point, and thus, allows quantifying the uncertainty of the recovered sound field.
Spatial reconstruction of the sound field in a room in the modal frequency range using Bayesian inference
Spatial characterization of the sound field in a room is a challenging task, as it usually requires a large number of measurement points. This paper presents a probabilistic approach for sound field reconstruction in the modal frequency range for small and medium-sized rooms based on Bayesian inference. A plane wave expansion model is used to decompose the sound field in the examined domain. The posterior distribution for the amplitude of each plane wave is inferred based on a uniform prior distribution with limits based on the maximum sound pressure observed in the measurements. Two different application cases are studied, namely a numerically computed sound field in a non-rectangular two-dimensional (2D) domain and a measured sound field in a horizontal evaluation area of a lightly damped room. The proposed reconstruction method provides an accurate reconstruction for both examined cases. Further, the results of Bayesian inference are compared to the reconstruction with a deterministic compressive sensing framework. The most significant advantage of the Bayesian method over deterministic reconstruction approaches is that it provides a probability distribution of the sound pressure at every reconstruction point, and thus, allows quantifying the uncertainty of the recovered sound field.
Spatial reconstruction of the sound field in a room in the modal frequency range using Bayesian inference
Schmid, Jonas M. (author) / Fernandez-Grande, Efren (author) / Hahmann, Manuel (author) / Gurbuz, Caglar (author) / Eser, Martin (author) / Marburg, Steffen (author)
2021-12-01
Schmid , J M , Fernandez-Grande , E , Hahmann , M , Gurbuz , C , Eser , M , Marburg , S & Schmid , J M 2021 , ' Spatial reconstruction of the sound field in a room in the modal frequency range using Bayesian inference ' , Journal of the Acoustical Society of America , vol. 150 , no. 6 , pp. 4385-4394 . https://doi.org/10.1121/10.0009040
Article (Journal)
Electronic Resource
English
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