Christensen, Mads Græsbøll

Jensen, Jesper Rindom

Jensen, Søren Holdt

Jensen, Tobias Lindstrøm

Nielsen, Jesper Kjær

Fundamental frequency estimation is a very important task in manyapplications involving periodic signals. For computational reasons,fast autocorrelation-based estimation methods are often used despitethat parametric estimation methods have a superior estimation accuracy.However, these parametric methods are much more costlyto run. In this paper, we propose an algorithm which significantlyreduces the cost of an accurate maximum likelihood-based estimatorfor real-valued data. The speed up is obtained by exploiting thematrix structure of the problem and by using a recursive solver. Viabenchmarks, we demonstrate that the computation time is reducedby approximately two orders of magnitude. The proposed fast algorithmis available online.Fundamental frequency estimation is a very important task in manyapplications involving periodic signals. For computational reasons,fast autocorrelation-based estimation methods are often used despiteparametric estimation methods having superior estimation accuracy.However, these parametric methods are much more costly to run. Inthis paper, we propose an algorithm which significantly reduces thecomputational cost of an accurate maximum likelihood-based estimatorfor real-valued data. The computational cost is reduced by exploitingthe matrix structure of the problem and by using a recursivesolver. Via benchmarks, we demonstrate that the computation timeis reduced by approximately two orders of magnitude. The proposedfast algorithm is available for download online

Fast and Statistically Efficient Fundamental Frequency Estimation

http://vbn.aau.dk/files/240352216/master.pdf

Fast and Statistically Efficient Fundamental Frequency Estimation

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