Multipitch Estimation Using Block Sparse Bayesian Learning and Intra-block Clustering

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

4 Citations (Scopus)
29 Downloads (Pure)

Abstract

Pitch estimation is an important task in speech and audio analysis. In this paper, we present a multi-pitch estimation algorithm based on block sparse Bayesian learning and intra-block clustering for speech analysis. A statistical hierarchical model is formulated based on a pitch dictionary with a fixed maximum number of harmonics for all the candidate pitches. Block sparse Bayesian learning is proposed for estimating the complex amplitudes. To deal with the problem of unknown harmonic orders and subharmonic errors, intra-block clustering structured sparsity prior is also introduced. The statis- tical update formulas are obtained by the variational Bayesian in- ference. Compared with the conventional group LASSO-type algo- rithms for multi-pitch estimation, experimental results indicate ro- bustness against noise and improved estimation accuracy of the pro- posed method.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages5
Place of PublicationCalgary, Canada
PublisherIEEE
Publication date15 Apr 2018
Pages666-670
ISBN (Print)978-1-5386-4657-1, 978-1-5386-4659-5
ISBN (Electronic)978-1-5386-4658-8
DOIs
Publication statusPublished - 15 Apr 2018
Event2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018
https://2018.ieeeicassp.org/

Conference

Conference2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
CountryCanada
CityCalgary
Period15/04/201820/04/2018
Internet address
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Fingerprint Dive into the research topics of 'Multipitch Estimation Using Block Sparse Bayesian Learning and Intra-block Clustering'. Together they form a unique fingerprint.

Cite this