Harmonics-to-noise ratio estimation with deterministically time-varying harmonic model for pathological voice signals

Takeshi Ikuma, Brad Story, Andrew J. McWhorter, Lacey Adkins, Melda Kunduk

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The harmonics-to-noise ratio (HNR) and other spectral noise parameters are important in clinical objective voice assessment as they could indicate the presence of nonharmonic phenomena, which are tied to the perception of hoarseness or breathiness. Existing HNR estimators are built on the voice signals to be nearly periodic (fixed over a short period), although voice pathology could induce involuntary slow modulation to void this assumption. This paper proposes the use of a deterministically time-varying harmonic model to improve the HNR measurements. To estimate the time-varying model, a two-stage iterative least squares algorithm is proposed to reduce model overfitting. The efficacy of the proposed HNR estimator is demonstrated with synthetic signals, simulated tremor signals, and recorded acoustic signals. Results indicate that the proposed algorithm can produce consistent HNR measures as the extent and rate of tremor are varied.

Original languageEnglish (US)
Pages (from-to)1783-1794
Number of pages12
JournalJournal of the Acoustical Society of America
Volume152
Issue number3
DOIs
StatePublished - Sep 1 2022

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Acoustics and Ultrasonics

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