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.
Original languageEnglish
Title of host publication23rd European Signal Processing Conference (EUSIPCO), 2015
PublisherIEEE Press
Publication date1 Sep 2015
Pages589 - 593
ISBN (Electronic)978-0-9928626-3-3
DOIs
Publication statusPublished - 1 Sep 2015
Event2015 23rd European Signal Processing Conference (EUSIPCO) - Nice, France
Duration: 31 Aug 20154 Sep 2015

Conference

Conference2015 23rd European Signal Processing Conference (EUSIPCO)
CountryFrance
CityNice
Period31/08/201504/09/2015
SeriesProceedings of the European Signal Processing Conference
ISSN2076-1465

Cite this

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. In 23rd European Signal Processing Conference (EUSIPCO), 2015 (pp. 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. pp. 589 - 593 (Proceedings of the European Signal Processing Conference).
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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.",
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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. in 23rd European Signal Processing Conference (EUSIPCO), 2015. IEEE Press, Proceedings of the European Signal Processing Conference, pp. 589 - 593, Nice, France, 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. p. 589 - 593 (Proceedings of the European Signal Processing Conference).

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

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N2 - 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. In 23rd European Signal Processing Conference (EUSIPCO), 2015. IEEE Press. 2015. p. 589 - 593. (Proceedings of the European Signal Processing Conference). https://doi.org/10.1109/EUSIPCO.2015.7362451