An Approximate Bayesian Fundamental Frequency Estimator

Jesper Kjær Nielsen, Mads Græsbøll Christensen, Søren Holdt Jensen

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4 Citationer (Scopus)
384 Downloads (Pure)

Abstrakt

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.
OriginalsprogEngelsk
Titel2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Antal sider4
ForlagIEEE Press
Publikationsdatomar. 2012
Sider4617-4620
ISBN (Trykt)978-1-4673-0045-2
ISBN (Elektronisk)978-1-4673-0044-5
DOI
StatusUdgivet - mar. 2012
BegivenhedIEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP - Kyoto, Japan
Varighed: 25 mar. 201230 mar. 2012

Konference

KonferenceIEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP
LandJapan
ByKyoto
Periode25/03/201230/03/2012
NavnI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

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