Literature DB >> 18537404

A spectral/temporal method for robust fundamental frequency tracking.

Stephen A Zahorian1, Hongbing Hu.   

Abstract

In this paper, a fundamental frequency (F(0)) tracking algorithm is presented that is extremely robust for both high quality and telephone speech, at signal to noise ratios ranging from clean speech to very noisy speech. The algorithm is named "YAAPT," for "yet another algorithm for pitch tracking." The algorithm is based on a combination of time domain processing, using the normalized cross correlation, and frequency domain processing. Major steps include processing of the original acoustic signal and a nonlinearly processed version of the signal, the use of a new method for computing a modified autocorrelation function that incorporates information from multiple spectral harmonic peaks, peak picking to select multiple F(0) candidates and associated figures of merit, and extensive use of dynamic programming to find the "best" track among the multiple F(0) candidates. The algorithm was evaluated by using three databases and compared to three other published F(0) tracking algorithms by using both high quality and telephone speech for various noise conditions. For clean speech, the error rates obtained are comparable to those obtained with the best results reported for any other algorithm; for noisy telephone speech, the error rates obtained are lower than those obtained with other methods.

Mesh:

Year:  2008        PMID: 18537404     DOI: 10.1121/1.2916590

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  6 in total

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2.  Acoustic Estimation of the Direction of Arrival of an Unmanned Aerial Vehicle Based on Frequency Tracking in the Time-Frequency Plane.

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Journal:  Sensors (Basel)       Date:  2022-05-26       Impact factor: 3.847

3.  Sensorimotor control of vocal pitch and formant frequencies in Parkinson's disease.

Authors:  Fatemeh Mollaei; Douglas M Shiller; Shari R Baum; Vincent L Gracco
Journal:  Brain Res       Date:  2016-06-08       Impact factor: 3.252

4.  Multiple levels of linguistic and paralinguistic features contribute to voice recognition.

Authors:  Jean Mary Zarate; Xing Tian; Kevin J P Woods; David Poeppel
Journal:  Sci Rep       Date:  2015-06-19       Impact factor: 4.379

5.  Cross-linguistic Influences on Sentence Accent Detection in Background Noise.

Authors:  Odette Scharenborg; Sofoklis Kakouros; Brechtje Post; Fanny Meunier
Journal:  Lang Speech       Date:  2019-01-04       Impact factor: 1.500

6.  A Robust and Low Computational Cost Pitch Estimation Method.

Authors:  Desheng Wang; Yangjie Wei; Yi Wang; Jing Wang
Journal:  Sensors (Basel)       Date:  2022-08-12       Impact factor: 3.847

  6 in total

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