Convex regularizations for the simultaneous recording of room impulse responses
Identifieur interne : 000D07 ( Main/Exploration ); précédent : 000D06; suivant : 000D08Convex regularizations for the simultaneous recording of room impulse responses
Auteurs : Alexis Benichoux [France] ; Laurent S. R. Simon [France] ; Emmanuel Vincent [France] ; Rémi Gribonval [France]Source :
- IEEE Transactions on Signal Processing [ 1053-587X ] ; 2014-01-03.
Abstract
We propose to acquire large sets of room impulse responses (RIRs) by simultaneously playing known source signals on multiple loudspeakers. We then estimate the RIRs via a convex optimization algorithm using convex penalties promoting sparsity and/or exponential amplitude envelope. We validate this approach on real-world recordings. The proposed algorithm makes it possible to estimate the RIRs to a reasonable accuracy even when the number of recorded samples is smaller than the number of RIR samples to be estimated, thereby leading to a speedup of the recording process compared to state-of-the-art RIR acquisition techniques. Moreover, the penalty promoting both sparsity and exponential amplitude envelope provides the best results in terms of robustness to the choice of its parameters, thereby consolidating the evidence in favor of sparse regularization for RIR estimation. Finally, the impact of the choice of the emitted signals is analyzed and evaluated.
Url:
DOI: 10.1109/TSP.2014.2303431
Affiliations:
- France
- Grand Est, Lorraine (région), Région Bretagne
- Lorient, Metz, Nancy, Rennes
- Université de Bretagne-Sud, Université de Lorraine, Université de Rennes 1, Université européenne de Bretagne
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Le document en format XML
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We then estimate the RIRs via a convex optimization algorithm using convex penalties promoting sparsity and/or exponential amplitude envelope. We validate this approach on real-world recordings. The proposed algorithm makes it possible to estimate the RIRs to a reasonable accuracy even when the number of recorded samples is smaller than the number of RIR samples to be estimated, thereby leading to a speedup of the recording process compared to state-of-the-art RIR acquisition techniques. Moreover, the penalty promoting both sparsity and exponential amplitude envelope provides the best results in terms of robustness to the choice of its parameters, thereby consolidating the evidence in favor of sparse regularization for RIR estimation. Finally, the impact of the choice of the emitted signals is analyzed and evaluated.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li><li>Région Bretagne</li></region><settlement><li>Lorient</li><li>Metz</li><li>Nancy</li><li>Rennes</li></settlement><orgName><li>Université de Bretagne-Sud</li><li>Université de Lorraine</li><li>Université de Rennes 1</li><li>Université européenne de Bretagne</li></orgName></list><tree><country name="France"><region name="Région Bretagne"><name sortKey="Benichoux, Alexis" sort="Benichoux, Alexis" uniqKey="Benichoux A" first="Alexis" last="Benichoux">Alexis Benichoux</name></region><name sortKey="Gribonval, Remi" sort="Gribonval, Remi" uniqKey="Gribonval R" first="Rémi" last="Gribonval">Rémi Gribonval</name><name sortKey="Simon, Laurent S R" sort="Simon, Laurent S R" uniqKey="Simon L" first="Laurent S. R." last="Simon">Laurent S. R. Simon</name><name sortKey="Vincent, Emmanuel" sort="Vincent, Emmanuel" uniqKey="Vincent E" first="Emmanuel" last="Vincent">Emmanuel Vincent</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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