Fast and Statistically Efficient Fundamental Frequency Estimation

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

Abstract

Fundamental frequency estimation is a very important task in many
applications involving periodic signals. For computational reasons,
fast autocorrelation-based estimation methods are often used despite
parametric estimation methods having superior estimation accuracy.
However, these parametric methods are much more costly to run. In
this paper, we propose an algorithm which significantly reduces the
computational cost of an accurate maximum likelihood-based estimator
for real-valued data. The computational cost is reduced by exploiting
the matrix structure of the problem and by using a recursive
solver. Via benchmarks, we demonstrate that the computation time
is reduced by approximately two orders of magnitude. The proposed
fast algorithm is available for download online.
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Details

Fundamental frequency estimation is a very important task in many
applications involving periodic signals. For computational reasons,
fast autocorrelation-based estimation methods are often used despite
parametric estimation methods having superior estimation accuracy.
However, these parametric methods are much more costly to run. In
this paper, we propose an algorithm which significantly reduces the
computational cost of an accurate maximum likelihood-based estimator
for real-valued data. The computational cost is reduced by exploiting
the matrix structure of the problem and by using a recursive
solver. Via benchmarks, we demonstrate that the computation time
is reduced by approximately two orders of magnitude. The proposed
fast algorithm is available for download online.
Original languageEnglish
Title of host publicationAcoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on
PublisherIEEE
Publication dateMar 2016
Pages86-90
ISBN (Electronic)978-1-4799-9988-0
DOI
StatePublished - Mar 2016
Publication categoryResearch
Peer-reviewedYes
EventThe 41st IEEE International Conference on Acoustics, Speech and Signal Processing - Shanghai, China
Duration: 20 Mar 201625 Mar 2016
http://www.icassp2016.org/

Conference

ConferenceThe 41st IEEE International Conference on Acoustics, Speech and Signal Processing
LandChina
ByShanghai
Periode20/03/201625/03/2016
Internetadresse
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

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