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Related Experiment Videos

Data labeling and sampling effects in harmonics-to-noise ratios.

N B Cox1, M R Ito, M D Morrison

  • 1Department of Electrical Engineering, University of British Columbia, Vancouver, Canada.

The Journal of the Acoustical Society of America
|May 1, 1989
PubMed
Summary

This study introduces improved algorithms for measuring the harmonics-to-noise ratio (HNR) in sustained vowels. The new methods reduce noise influence and correct errors for more accurate voice analysis.

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Area of Science:

  • Acoustic phonetics
  • Speech science
  • Signal processing

Background:

  • The harmonics-to-noise ratio (HNR) is crucial for quantifying noise in sustained vowels.
  • Existing methods for HNR measurement can be sensitive to data offsets, jitter, and shimmer.

Purpose of the Study:

  • To develop a generalized algorithm for HNR measurement that is robust to data offsets, jitter, and shimmer.
  • To propose a new algorithm capable of handling periodic perturbations.
  • To analyze and correct errors in pitch-period demarcation.

Main Methods:

  • A generalized algorithm was developed to remove data offset dependence and reduce jitter/shimmer influence.
  • A novel algorithm was introduced to accommodate periodic perturbations.
  • Mathematical analysis was performed on pitch-period demarcation errors, and a correction method was devised.

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Main Results:

  • The generalized algorithm simplifies HNR analysis by enabling single-pass computation under specific assumptions.
  • Analysis revealed that high-frequency components increase sensitivity to demarcation errors, predicting underestimation for /a/ and /i/ vowels.
  • The proposed error correction method effectively reduces demarcation errors for most waveforms.

Conclusions:

  • The developed algorithms provide a more accurate and efficient method for HNR estimation in sustained vowels.
  • The error correction technique enhances the reliability of HNR measurements, particularly for less perturbed voice signals.
  • This work offers significant improvements for quantitative voice analysis and acoustic research.