Robust Fundamental Frequency Estimation in Coloured Noise
Alfredo Esquivel Jaramillo, Andreas Jakobsson, Jesper Kjær Nielsen, Mads Græsbøll Christensen
Abstract
Most parametric fundamental frequency estimators make the implicit assumption that any corrupting noise is additive, white Gaus-sian. Under this assumption, the maximum likelihood (ML) and the least squares estimators are the same, and statistically efficient. However, in the coloured noise case, the estimators differ, and the spectral shape of the corrupting noise should be taken into account. To allow for this, we here propose two schemes that refine the noise statistics and parameter estimates in an iterative manner, one of them based on an approximate ML solution and the other one based on removing the periodic signal obtained from a linearly constrained minimum variance (LCMV) filter. Evaluations on real speech data indicate that the iteration steps improve the estimation accuracy, therefore offering improvement over traditional non-parametric fundamental frequency methods in most of the evaluated scenarios.
BibTeX
@inproceedings{icassp2020_robustfundamenta,
title = {Robust Fundamental Frequency Estimation in Coloured Noise},
author = {Alfredo Esquivel Jaramillo and Andreas Jakobsson and Jesper Kjær Nielsen and Mads Græsbøll Christensen},
booktitle = {ICASSP 2020},
year = {2020}
}