Multi-Pitch Estimation and Tracking Using Bayesian Inference in Block Sparsity

Sam Karimian-Azari, Andreas Jakobsson, Jesper Rindom Jensen, Mads Græsbøll Christensen

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4 Citationer (Scopus)
529 Downloads (Pure)

Abstrakt

In this paper, we consider the problem of multi-pitch estimation and tracking of an unknown number of harmonic audio sources. The regularized least-squares is a solution for simultaneous sparse source selection and parameter estimation. Exploiting block sparsity, the method allows for reliable tracking of the found sources, without posing detailed a priori assumptions of the number of harmonics for each source. The method incorporates a Bayesian prior and assigns data-dependent regularization coefficients to efficiently incorporate both earlier and future data blocks in the tracking of estimates. In comparison with fix regularization coefficients, the simulation results, using both real and synthetic audio signals, confirm the performance of the proposed method.
OriginalsprogEngelsk
Titel2015 Proceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015)
ForlagIEEE
Publikationsdato2015
Sider16-20
ISBN (Trykt)978-0-9928626-3-3
DOI
StatusUdgivet - 2015
BegivenhedEuropean Signal Processing Conference (EUSIPCO) - Nice, Frankrig
Varighed: 31 aug. 20154 sep. 2015

Konference

KonferenceEuropean Signal Processing Conference (EUSIPCO)
LandFrankrig
ByNice
Periode31/08/201504/09/2015
NavnProceedings of the European Signal Processing Conference
ISSN2076-1465

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