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Performance Evaluation of Subharmonic-to-Harmonic Ratio (SHR) Computation.

Christian T Herbst1

  • 1Antonio Salieri Department of Vocal Studies and Vocal Research in Music Education, University of Music and Performing Arts Vienna, Vienna, Austria.

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|March 14, 2020
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Summary

The subharmonic-to-harmonic ratio (SHR) algorithm effectively detects subharmonics in voice signals with adaptive settings. Optimal performance requires specific parameter tuning for frequency ceiling and frame length, recommending SHR ≥ 0.01 for classification.

Keywords:
EGGElectroglottographyPeriod doublingSHRSubharmonic-to-harmonic ratioSubharmonicsVoice

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Area of Science:

  • Acoustics
  • Signal Processing
  • Bioacoustics

Background:

  • Subharmonics are crucial in voice signals for speech, singing, and animal vocalizations.
  • They originate from amplitude (AM) or frequency modulation (FM) of time-domain signals.
  • Currently, Sun's subharmonic-to-harmonic ratio (SHR) is the sole open-source subharmonics detector.

Purpose of the Study:

  • To formally evaluate the performance of the SHR algorithm for subharmonic detection.
  • To assess the algorithm's robustness across synthesized and empirical voice data.
  • To determine optimal parameter settings for accurate subharmonic classification.

Main Methods:

  • Evaluation using two datasets: synthesized electroglottographic (EGG) signals (n=2560) and empirical EGG samples from the CMU Arctic database (n=25).
  • Synthesized data varied in AM/FM modulation, fundamental frequency (f0), periodicity, and signal-to-noise ratio (SNR).
  • Empirical data underwent manual annotation by five experts to establish ground truth.

Main Results:

  • SHR demonstrated robustness in synthesized data with modulation extents below 0.35 (FM) and 0.7 (AM).
  • For empirical data, adaptive SHR settings achieved 87% sensitivity and >90% specificity.
  • Default SHR parameters showed poor performance, classifying only 9% of subharmonic instances.

Conclusions:

  • The SHR algorithm is a valuable tool for assessing subharmonics in voice signals when parameters are adaptively tuned.
  • Recommended adaptive settings include a frequency ceiling of 5x highest f0 and frame length of at least 5x largest fundamental period.
  • A threshold of SHR ≥ 0.01 is suggested for reliable subharmonic classification.