TY - GEN
T1 - Sketching for nearfield acoustic imaging of heavy-tailed sources
AU - Fontaine, Mathieu
AU - Vanwynsberghe, Charles
AU - Liutkus, Antoine
AU - Badeau, Roland
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - We propose a probabilistic model for acoustic source localization with known but arbitrary geometry of the microphone array. The approach has several features. First, it relies on a simple nearfield acoustic model for wave propagation. Second, it does not require the number of active sources. On the contrary, it produces a heat map representing the energy of a large set of candidate locations, thus imaging the acoustic field. Second, it relies on a heavy-tail α-stable probabilistic model, whose most important feature is to yield an estimation strategy where the multichannel signals need to be processed only once in a simple online procedure, called sketching. This sketching produces a fixed-sized representation of the data that is then analyzed for localization. The resulting algorithm has a small computational complexity and in this paper, we demonstrate that it compares favorably with state of the art for localization in realistic simulations of reverberant environments.
AB - We propose a probabilistic model for acoustic source localization with known but arbitrary geometry of the microphone array. The approach has several features. First, it relies on a simple nearfield acoustic model for wave propagation. Second, it does not require the number of active sources. On the contrary, it produces a heat map representing the energy of a large set of candidate locations, thus imaging the acoustic field. Second, it relies on a heavy-tail α-stable probabilistic model, whose most important feature is to yield an estimation strategy where the multichannel signals need to be processed only once in a simple online procedure, called sketching. This sketching produces a fixed-sized representation of the data that is then analyzed for localization. The resulting algorithm has a small computational complexity and in this paper, we demonstrate that it compares favorably with state of the art for localization in realistic simulations of reverberant environments.
UR - https://www.scopus.com/pages/publications/85013422784
U2 - 10.1007/978-3-319-53547-0_8
DO - 10.1007/978-3-319-53547-0_8
M3 - Conference contribution
AN - SCOPUS:85013422784
SN - 9783319535463
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 80
EP - 88
BT - Latent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings
A2 - Tichavsky, Petr
A2 - Babaie-Zadeh, Massoud
A2 - Michel, Olivier J.J.
A2 - Thirion-Moreau, Nadege
PB - Springer Verlag
T2 - 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017
Y2 - 21 February 2017 through 23 February 2017
ER -