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 significantly the estimation accuracy of an NLS pitch estimator 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 publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Number of pages5
PublisherIEEE
Publication dateMay 2019
Pages6495-6499
Article number8683653
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - May 2019
Event2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Brighton, United Kingdom
Duration: 12 May 201917 May 2019
https://2019.ieeeicassp.org/

Conference

Conference2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/201917/05/2019
Internet address
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Keywords

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

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