Fast and Statistically Efficient Fundamental Frequency Estimation

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
that parametric estimation methods have a superior estimation accuracy.
However, these parametric methods are much more costly
to run. In this paper, we propose an algorithm which significantly
reduces the cost of an accurate maximum likelihood-based estimator
for real-valued data. The speed up is obtained 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 online.