A Fast Algorithm for Maximum Likelihood-based Fundamental Frequency Estimation

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

Abstract


Print
Request Permissions
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.
Close

Details


Print
Request Permissions
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
DOI
StatePublished - 1 Sep 2015
Publication categoryResearch
Peer-reviewedYes
Event2015 23rd European Signal Processing Conference (EUSIPCO) - Nice, France
Duration: 31 Aug 20154 Sep 2015

Conference

Conference2015 23rd European Signal Processing Conference (EUSIPCO)
LandFrance
ByNice
Periode31/08/201504/09/2015
SeriesProceedings of the European Signal Processing Conference
ISSN2076-1465

Download statistics

No data available
ID: 224779720