Fast and Statistically Efficient Fundamental Frequency Estimation

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5 Citationer (Scopus)
310 Downloads (Pure)

Abstrakt

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
that parametric estimation methods have a superior estimation accuracy.
However, these parametric methods are much more costly
to run. In this paper, we propose an algorithm which significantly
reduces the cost of an accurate maximum likelihood-based estimator
for real-valued data. The speed up is obtained 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 online.
OriginalsprogEngelsk
TitelAcoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on
ForlagIEEE
Publikationsdatomar. 2016
Sider86-90
ISBN (Elektronisk)978-1-4799-9988-0
DOI
StatusUdgivet - mar. 2016
BegivenhedThe 41st IEEE International Conference on Acoustics, Speech and Signal Processing - Shanghai, Kina
Varighed: 20 mar. 201625 mar. 2016
http://www.icassp2016.org/

Konference

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

Emneord

  • fundamental frequency estimation
  • Toeplitz-plus-Hankel solver
  • fast algorithm

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  • Projekter

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    Projekter: ProjektForskning

    Citationsformater

    Nielsen, J. K., Jensen, T. L., Jensen, J. R., Christensen, M. G., & Jensen, S. H. (2016). Fast and Statistically Efficient Fundamental Frequency Estimation. I Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on (s. 86-90). IEEE. I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings https://doi.org/10.1109/ICASSP.2016.7471642