An Approximate Bayesian Fundamental Frequency Estimator

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

3 Citations (Scopus)
321 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

Fingerprint

Maximum likelihood
Processing

Cite this

Nielsen, J. K., Christensen, M. G., & Jensen, S. H. (2012). An Approximate Bayesian Fundamental Frequency Estimator. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4617-4620). IEEE Press. I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings https://doi.org/10.1109/ICASSP.2012.6288947
Nielsen, Jesper Kjær ; Christensen, Mads Græsbøll ; Jensen, Søren Holdt. / An Approximate Bayesian Fundamental Frequency Estimator. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE Press, 2012. pp. 4617-4620 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).
@inproceedings{283f5405ee124ddeb533f0c13964b13e,
title = "An Approximate Bayesian Fundamental Frequency Estimator",
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.",
author = "Nielsen, {Jesper Kj{\ae}r} and Christensen, {Mads Gr{\ae}sb{\o}ll} and Jensen, {S{\o}ren Holdt}",
year = "2012",
month = "3",
doi = "10.1109/ICASSP.2012.6288947",
language = "English",
isbn = "978-1-4673-0045-2",
series = "I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings",
publisher = "IEEE Press",
pages = "4617--4620",
booktitle = "2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

}

Nielsen, JK, Christensen, MG & Jensen, SH 2012, An Approximate Bayesian Fundamental Frequency Estimator. in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE Press, I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings, pp. 4617-4620, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing, Kyoto, Japan, 25/03/2012. https://doi.org/10.1109/ICASSP.2012.6288947

An Approximate Bayesian Fundamental Frequency Estimator. / Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt.

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE Press, 2012. p. 4617-4620 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).

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

TY - GEN

T1 - An Approximate Bayesian Fundamental Frequency Estimator

AU - Nielsen, Jesper Kjær

AU - Christensen, Mads Græsbøll

AU - Jensen, Søren Holdt

PY - 2012/3

Y1 - 2012/3

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84867619223&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2012.6288947

DO - 10.1109/ICASSP.2012.6288947

M3 - Article in proceeding

SN - 978-1-4673-0045-2

T3 - I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings

SP - 4617

EP - 4620

BT - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE Press

ER -

Nielsen JK, Christensen MG, Jensen SH. An Approximate Bayesian Fundamental Frequency Estimator. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE Press. 2012. p. 4617-4620. (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings). https://doi.org/10.1109/ICASSP.2012.6288947