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Robust Fundamental Frequency Estimation in Coloured Noise

Jaramillo, Alfredo Esquivel LU ; Jakobsson, Andreas LU orcid ; Nielsen, Jesper Kjar and Grasboll Christensen, Mads (2020) 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2020-May. p.741-745
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... (More)

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.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
coloured noise, fundamental frequency, LCMV filter, least-squares, maximum likelihood, pre-whitening
host publication
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
series title
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
volume
2020-May
article number
9053018
pages
5 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
conference location
Barcelona, Spain
conference dates
2020-05-04 - 2020-05-08
external identifiers
  • scopus:85089237075
ISSN
1520-6149
ISBN
9781509066315
DOI
10.1109/ICASSP40776.2020.9053018
language
English
LU publication?
yes
id
78621d4f-cd2f-4a5a-a3b3-13348cb571b6
date added to LUP
2020-08-19 08:42:55
date last changed
2022-04-19 00:13:10
@inproceedings{78621d4f-cd2f-4a5a-a3b3-13348cb571b6,
  abstract     = {{<p>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.</p>}},
  author       = {{Jaramillo, Alfredo Esquivel and Jakobsson, Andreas and Nielsen, Jesper Kjar and Grasboll Christensen, Mads}},
  booktitle    = {{2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings}},
  isbn         = {{9781509066315}},
  issn         = {{1520-6149}},
  keywords     = {{coloured noise; fundamental frequency; LCMV filter; least-squares; maximum likelihood; pre-whitening}},
  language     = {{eng}},
  pages        = {{741--745}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}},
  title        = {{Robust Fundamental Frequency Estimation in Coloured Noise}},
  url          = {{http://dx.doi.org/10.1109/ICASSP40776.2020.9053018}},
  doi          = {{10.1109/ICASSP40776.2020.9053018}},
  volume       = {{2020-May}},
  year         = {{2020}},
}