Sound field reproduction using the lasso

Georgios N. Lilis, Daniele Angelosante, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

111 Scopus citations


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.

Original languageEnglish (US)
Article number5443604
Pages (from-to)1902-1912
Number of pages11
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number8
StatePublished - 2010

Bibliographical note

Funding Information:
Manuscript received August 06, 2009; revised December 04, 2009. Date of publication April 05, 2010; date of current version September 01, 2010. This work was supported by the National Science Foundation under Grants CCF 0830480 and CON 014658. Part of this paper was presented at the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, Oct. 2009. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Nakatani Tomohiro.


  • Lasso
  • Sound reproduction
  • compressive sampling
  • least-squares (LS)
  • loudspeaker positioning
  • sparse regression


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