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
Publication statusPublished - 1 Sep 2015
Event2015 23rd European Signal Processing Conference (EUSIPCO) - Nice, France
Duration: 31 Aug 20154 Sep 2015


Conference2015 23rd European Signal Processing Conference (EUSIPCO)
SeriesProceedings of the European Signal Processing Conference

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