Robust Fundamental Frequency Estimation in Coloured Noise

Alfredo Esquivel Jaramillo, Andreas Jakobsson, Jesper Kjær Nielsen, Mads Græsbøll Christensen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

1 Citationer (Scopus)
112 Downloads (Pure)

Abstrakt

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.
OriginalsprogEngelsk
Titel2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
ForlagIEEE
Publikationsdato2020
Artikelnummer9053018
ISBN (Trykt)978-1-5090-6632-2
ISBN (Elektronisk)978-1-5090-6631-5
DOI
StatusUdgivet - 2020
Begivenhed2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spanien
Varighed: 4 maj 20208 maj 2020

Konference

Konference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
LandSpanien
ByBarcelona
Periode04/05/202008/05/2020
SponsorThe Institute of Electrical and Electronics Engineers, Signal Processing Society
NavnI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

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