Fast and statistically efficient fundamental frequency estimation

Abstract

Fundamental frequency estimation is a very important task in many applications involving periodic signals. For computational reasons, fast autocorrelation-based estimation methods are often used despite parametric estimation methods having superior estimation accuracy. However, these parametric methods are much more costly to run. In this paper, we propose an algorithm which significantly reduces the computational cost of an accurate maximum likelihood-based estimator for real-valued data. The computational cost is reduced by exploiting the matrix structure of the problem and by using a recursive solver. Via benchmarks, we demonstrate that the computation time is reduced by approximately two orders of magnitude. The proposed fast algorithm is available for download online.