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Robust Fundamental Frequency Estimation in Coloured Noise

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Citation Author(s):
Andreas Jakobsson, Mads Græsbøll Christensen
Submitted by:
Alfredo Esquive...
Last updated:
8 May 2020 - 12:48pm
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Alfredo Esquivel Jaramillo
Paper Code:
AUD-P9.9

Abstract 

Abstract: 

Most parametric fundamental frequency estimators make the implicit assumption that any corrupting noise is additive, white Gaussian. 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.

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Dataset Files

pitch in colored noise

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