A Fast Algorithm for Maximum Likelihood-based Fundamental Frequency Estimation

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Periodic signals are encountered in many applications. Such signals can be modelled by a weighted sum of sinusoidal components whose frequencies are integer multiples of a fundamental frequency. Given a data set, the fundamental frequency can be estimated in many ways including a maximum likelihood (ML) approach. Unfortunately, the ML estimator has a very high computational complexity, and the more inaccurate, but faster correlation-based estimators are therefore often used instead. In this paper, we propose a fast algorithm for the evaluation of the ML cost function for complex-valued data over all frequencies on a Fourier grid and up to a maximum model order. The proposed algorithm significantly reduces the computational complexity to a level not far from the complexity of the popular harmonic summation method which is an approximate ML estimator.
OriginalsprogEngelsk
Titel23rd European Signal Processing Conference (EUSIPCO), 2015
ForlagIEEE Press
Publikationsdato1 sep. 2015
Sider589 - 593
ISBN (Elektronisk)978-0-9928626-3-3
DOI
StatusUdgivet - 1 sep. 2015
Begivenhed2015 23rd European Signal Processing Conference (EUSIPCO) - Nice, Frankrig
Varighed: 31 aug. 20154 sep. 2015

Konference

Konference2015 23rd European Signal Processing Conference (EUSIPCO)
LandFrankrig
ByNice
Periode31/08/201504/09/2015
NavnProceedings of the European Signal Processing Conference
ISSN2076-1465

Citer dette

Nielsen, J. K., Jensen, T. L., Jensen, J. R., Christensen, M. G., & Jensen, S. H. (2015). A Fast Algorithm for Maximum Likelihood-based Fundamental Frequency Estimation. I 23rd European Signal Processing Conference (EUSIPCO), 2015 (s. 589 - 593). IEEE Press. Proceedings of the European Signal Processing Conference https://doi.org/10.1109/EUSIPCO.2015.7362451
Nielsen, Jesper Kjær ; Jensen, Tobias Lindstrøm ; Jensen, Jesper Rindom ; Christensen, Mads Græsbøll ; Jensen, Søren Holdt. / A Fast Algorithm for Maximum Likelihood-based Fundamental Frequency Estimation. 23rd European Signal Processing Conference (EUSIPCO), 2015. IEEE Press, 2015. s. 589 - 593 (Proceedings of the European Signal Processing Conference).
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title = "A Fast Algorithm for Maximum Likelihood-based Fundamental Frequency Estimation",
abstract = "PrintRequest PermissionsPeriodic signals are encountered in many applications. Such signals can be modelled by a weighted sum of sinusoidal components whose frequencies are integer multiples of a fundamental frequency. Given a data set, the fundamental frequency can be estimated in many ways including a maximum likelihood (ML) approach. Unfortunately, the ML estimator has a very high computational complexity, and the more inaccurate, but faster correlation-based estimators are therefore often used instead. In this paper, we propose a fast algorithm for the evaluation of the ML cost function for complex-valued data over all frequencies on a Fourier grid and up to a maximum model order. The proposed algorithm significantly reduces the computational complexity to a level not far from the complexity of the popular harmonic summation method which is an approximate ML estimator.",
author = "Nielsen, {Jesper Kj{\ae}r} and Jensen, {Tobias Lindstr{\o}m} and Jensen, {Jesper Rindom} and Christensen, {Mads Gr{\ae}sb{\o}ll} and Jensen, {S{\o}ren Holdt}",
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Nielsen, JK, Jensen, TL, Jensen, JR, Christensen, MG & Jensen, SH 2015, A Fast Algorithm for Maximum Likelihood-based Fundamental Frequency Estimation. i 23rd European Signal Processing Conference (EUSIPCO), 2015. IEEE Press, Proceedings of the European Signal Processing Conference, s. 589 - 593, 2015 23rd European Signal Processing Conference (EUSIPCO), Nice, Frankrig, 31/08/2015. https://doi.org/10.1109/EUSIPCO.2015.7362451

A Fast Algorithm for Maximum Likelihood-based Fundamental Frequency Estimation. / Nielsen, Jesper Kjær; Jensen, Tobias Lindstrøm; Jensen, Jesper Rindom; Christensen, Mads Græsbøll; Jensen, Søren Holdt.

23rd European Signal Processing Conference (EUSIPCO), 2015. IEEE Press, 2015. s. 589 - 593 (Proceedings of the European Signal Processing Conference).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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AU - Jensen, Søren Holdt

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AB - PrintRequest PermissionsPeriodic signals are encountered in many applications. Such signals can be modelled by a weighted sum of sinusoidal components whose frequencies are integer multiples of a fundamental frequency. Given a data set, the fundamental frequency can be estimated in many ways including a maximum likelihood (ML) approach. Unfortunately, the ML estimator has a very high computational complexity, and the more inaccurate, but faster correlation-based estimators are therefore often used instead. In this paper, we propose a fast algorithm for the evaluation of the ML cost function for complex-valued data over all frequencies on a Fourier grid and up to a maximum model order. The proposed algorithm significantly reduces the computational complexity to a level not far from the complexity of the popular harmonic summation method which is an approximate ML estimator.

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Nielsen JK, Jensen TL, Jensen JR, Christensen MG, Jensen SH. A Fast Algorithm for Maximum Likelihood-based Fundamental Frequency Estimation. I 23rd European Signal Processing Conference (EUSIPCO), 2015. IEEE Press. 2015. s. 589 - 593. (Proceedings of the European Signal Processing Conference). https://doi.org/10.1109/EUSIPCO.2015.7362451