TY - JOUR
T1 - Sound field reproduction using the lasso
AU - Lilis, Georgios N.
AU - Angelosante, Daniele
AU - Giannakis, Georgios B.
PY - 2010/9/9
Y1 - 2010/9/9
N2 - Reproducing a sampled sound field using an array of loudspeakers is a problem with well-appreciated applications to acoustics and ultrasound treatment. Loudspeaker signal design has traditionally relied on (possibly regularized) least-squares (LS) criteria. In many cases however, the desired sound field can be reproduced using only a few loudspeakers, which are sparsely distributed in space. To exploit this feature, the fresh look advocated here permeates benefits from advances in variable selection and compressive sampling to sound field synthesis by formulating a sparse linear regression problem that is solved using the least-absolute shrinkage and selection operator (Lasso). An efficient implementation of the Lasso for the problem at hand is developed based on a coordinate descent iteration. Analysis and simulations demonstrate that Lasso-based sound field reproduction yields better performance than LS especially at high frequencies and for reproduction of under-sampled sound fields. In addition, Lasso-based synthesis enables judicious placement of loudspeaker arrays.
AB - Reproducing a sampled sound field using an array of loudspeakers is a problem with well-appreciated applications to acoustics and ultrasound treatment. Loudspeaker signal design has traditionally relied on (possibly regularized) least-squares (LS) criteria. In many cases however, the desired sound field can be reproduced using only a few loudspeakers, which are sparsely distributed in space. To exploit this feature, the fresh look advocated here permeates benefits from advances in variable selection and compressive sampling to sound field synthesis by formulating a sparse linear regression problem that is solved using the least-absolute shrinkage and selection operator (Lasso). An efficient implementation of the Lasso for the problem at hand is developed based on a coordinate descent iteration. Analysis and simulations demonstrate that Lasso-based sound field reproduction yields better performance than LS especially at high frequencies and for reproduction of under-sampled sound fields. In addition, Lasso-based synthesis enables judicious placement of loudspeaker arrays.
KW - Lasso
KW - Sound reproduction
KW - compressive sampling
KW - least-squares (LS)
KW - loudspeaker positioning
KW - sparse regression
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U2 - 10.1109/TASL.2010.2040523
DO - 10.1109/TASL.2010.2040523
M3 - Article
AN - SCOPUS:77956277671
VL - 18
SP - 1902
EP - 1912
JO - IEEE Transactions on Speech and Audio Processing
JF - IEEE Transactions on Speech and Audio Processing
SN - 1558-7916
IS - 8
M1 - 5443604
ER -