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

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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

6 Citations (Scopus)
624 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publication2015 Proceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015)
PublisherIEEE
Publication date2015
Pages16-20
ISBN (Print)978-0-9928626-3-3
DOIs
Publication statusPublished - 2015
EventEuropean Signal Processing Conference (EUSIPCO) - Nice, France
Duration: 31 Aug 20154 Sept 2015

Conference

ConferenceEuropean Signal Processing Conference (EUSIPCO)
Country/TerritoryFrance
CityNice
Period31/08/201504/09/2015
SeriesProceedings of the European Signal Processing Conference
ISSN2076-1465

Keywords

  • Multi-pitch estimation
  • tracking
  • harmonic signal
  • regularized least-squares
  • sparsity

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