An Approximate Bayesian Fundamental Frequency Estimator

Jesper Kjær Nielsen, Mads Græsbøll Christensen, Søren Holdt Jensen

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

4 Citations (Scopus)
384 Downloads (Pure)

Abstract

Joint fundamental frequency and model order estimation is an important problem in several applications such as speech and music processing. In this paper, we develop an approximate estimation algorithm of these quantities using Bayesian inference. The inference about the fundamental frequency and the model order is based on a probability model which corresponds to a minimum of prior information. From this probability model, we give the exact posterior distributions on the fundamental frequency and the model order, and we also present analytical approximations of these distributions which lower the computational load of the algorithm. By use of simulations on both a synthetic signal and a speech signal, the algorithm is demonstrated to be more accurate than a state-of-the-art maximum likelihood-based method.
Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages4
PublisherIEEE Press
Publication dateMar 2012
Pages4617-4620
ISBN (Print)978-1-4673-0045-2
ISBN (Electronic)978-1-4673-0044-5
DOIs
Publication statusPublished - Mar 2012
Event2012 IEEE International Conference on Acoustics, Speech and Signal Processing - Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012

Conference

Conference2012 IEEE International Conference on Acoustics, Speech and Signal Processing
CountryJapan
CityKyoto
Period25/03/201230/03/2012
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

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