A Study on how Pre-whitening Influences Fundamental Frequency Estimation

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Abstract

This paper deals with the influence of pre-whitening for the task of fundamental frequency estimation in noisy conditions. Parametric fundamental frequency estimators commonly assume that the noise is white and Gaussian and, therefore, they are only statistically efficient under those conditions. The noise is coloured in many practical applications and this will often result in problems of misidentifying an integer divisor or multiple of the true fundamental frequency (i.e., octave errors). The purpose of this paper is to see if pre-whitening can reduce this problem, based on noise statistics obtained from existing noise PSD estimation algorithms. For this purpose, different noise types and prediction orders of LPC pre-whitening are considered. The results show that pre-whitening improves the estimation accuracy of an NLS pitch estimator significantly when the noise is fairly stationary. For nonstationary noise, the improvements are modest at best, but we hypothesize that this is due to the noise PSD estimation performance rather than the LPC pre-whitening principle.
Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing
Publication date2019
DOIs
Publication statusPublished - 2019

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Frequency estimation
Gaussian noise (electronic)
White noise
Statistics

Keywords

  • fundamental frequency
  • pre-whitening
  • spectral flatness measure
  • gross error rate

Cite this

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title = "A Study on how Pre-whitening Influences Fundamental Frequency Estimation",
abstract = "This paper deals with the influence of pre-whitening for the task of fundamental frequency estimation in noisy conditions. Parametric fundamental frequency estimators commonly assume that the noise is white and Gaussian and, therefore, they are only statistically efficient under those conditions. The noise is coloured in many practical applications and this will often result in problems of misidentifying an integer divisor or multiple of the true fundamental frequency (i.e., octave errors). The purpose of this paper is to see if pre-whitening can reduce this problem, based on noise statistics obtained from existing noise PSD estimation algorithms. For this purpose, different noise types and prediction orders of LPC pre-whitening are considered. The results show that pre-whitening improves the estimation accuracy of an NLS pitch estimator significantly when the noise is fairly stationary. For nonstationary noise, the improvements are modest at best, but we hypothesize that this is due to the noise PSD estimation performance rather than the LPC pre-whitening principle.",
keywords = "fundamental frequency, pre-whitening, spectral flatness measure, gross error rate",
author = "{Esquivel Jaramillo}, Alfredo and Nielsen, {Jesper Kj{\ae}r} and Christensen, {Mads Gr{\ae}sb{\o}ll}",
year = "2019",
doi = "10.1109/ICASSP.2019.8683653",
language = "English",
booktitle = "IEEE International Conference on Acoustics, Speech and Signal Processing",

}

A Study on how Pre-whitening Influences Fundamental Frequency Estimation. / Esquivel Jaramillo, Alfredo; Nielsen, Jesper Kjær; Christensen, Mads Græsbøll.

IEEE International Conference on Acoustics, Speech and Signal Processing. 2019.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

TY - GEN

T1 - A Study on how Pre-whitening Influences Fundamental Frequency Estimation

AU - Esquivel Jaramillo, Alfredo

AU - Nielsen, Jesper Kjær

AU - Christensen, Mads Græsbøll

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N2 - This paper deals with the influence of pre-whitening for the task of fundamental frequency estimation in noisy conditions. Parametric fundamental frequency estimators commonly assume that the noise is white and Gaussian and, therefore, they are only statistically efficient under those conditions. The noise is coloured in many practical applications and this will often result in problems of misidentifying an integer divisor or multiple of the true fundamental frequency (i.e., octave errors). The purpose of this paper is to see if pre-whitening can reduce this problem, based on noise statistics obtained from existing noise PSD estimation algorithms. For this purpose, different noise types and prediction orders of LPC pre-whitening are considered. The results show that pre-whitening improves the estimation accuracy of an NLS pitch estimator significantly when the noise is fairly stationary. For nonstationary noise, the improvements are modest at best, but we hypothesize that this is due to the noise PSD estimation performance rather than the LPC pre-whitening principle.

AB - This paper deals with the influence of pre-whitening for the task of fundamental frequency estimation in noisy conditions. Parametric fundamental frequency estimators commonly assume that the noise is white and Gaussian and, therefore, they are only statistically efficient under those conditions. The noise is coloured in many practical applications and this will often result in problems of misidentifying an integer divisor or multiple of the true fundamental frequency (i.e., octave errors). The purpose of this paper is to see if pre-whitening can reduce this problem, based on noise statistics obtained from existing noise PSD estimation algorithms. For this purpose, different noise types and prediction orders of LPC pre-whitening are considered. The results show that pre-whitening improves the estimation accuracy of an NLS pitch estimator significantly when the noise is fairly stationary. For nonstationary noise, the improvements are modest at best, but we hypothesize that this is due to the noise PSD estimation performance rather than the LPC pre-whitening principle.

KW - fundamental frequency

KW - pre-whitening

KW - spectral flatness measure

KW - gross error rate

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DO - 10.1109/ICASSP.2019.8683653

M3 - Article in proceeding

BT - IEEE International Conference on Acoustics, Speech and Signal Processing

ER -