Classification of normal and pathological voices using TEO phase and Mel cepstral features

Hemant A. Patil, Pallavi N. Baljekar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, a new feature-set, viz., Teager Energy Operator (TEO) phase has been proposed for automatic classification of normal vs. pathological voices. Development of TEO phase has been motivated from recently proposed linear prediction (LP) residual phase for speaker recognition. Classification was performed using a discriminatively-trained 2 nd order polynomial classifier on a subset of the Massachusetts Ear and Eye Infirmary (MEEI) database. Score-level fusion of TEO phase and state-of-the-art Mel frequency cepstral coefficients (MFCC) gave reduction in equal error rate (EER) by 1.86 % than EER of MFCC alone. Proposed TEO phase feature set is also evaluated under degraded conditions using the NOISEX-92 database for the case of additive car noise.

Original languageEnglish
Title of host publication2012 International Conference on Signal Processing and Communications, SPCOM 2012
DOIs
Publication statusPublished - 26-10-2012
Externally publishedYes
Event2012 9th International Conference on Signal Processing and Communications, SPCOM 2012 - Bangalore, India
Duration: 22-07-201225-07-2012

Conference

Conference2012 9th International Conference on Signal Processing and Communications, SPCOM 2012
CountryIndia
CityBangalore
Period22-07-1225-07-12

Fingerprint

energy
Set theory
Acoustic noise
Classifiers
Fusion reactions
Polynomials

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Communication

Cite this

Patil, H. A., & Baljekar, P. N. (2012). Classification of normal and pathological voices using TEO phase and Mel cepstral features. In 2012 International Conference on Signal Processing and Communications, SPCOM 2012 [6289991] https://doi.org/10.1109/SPCOM.2012.6289991
Patil, Hemant A. ; Baljekar, Pallavi N. / Classification of normal and pathological voices using TEO phase and Mel cepstral features. 2012 International Conference on Signal Processing and Communications, SPCOM 2012. 2012.
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abstract = "In this paper, a new feature-set, viz., Teager Energy Operator (TEO) phase has been proposed for automatic classification of normal vs. pathological voices. Development of TEO phase has been motivated from recently proposed linear prediction (LP) residual phase for speaker recognition. Classification was performed using a discriminatively-trained 2 nd order polynomial classifier on a subset of the Massachusetts Ear and Eye Infirmary (MEEI) database. Score-level fusion of TEO phase and state-of-the-art Mel frequency cepstral coefficients (MFCC) gave reduction in equal error rate (EER) by 1.86 {\%} than EER of MFCC alone. Proposed TEO phase feature set is also evaluated under degraded conditions using the NOISEX-92 database for the case of additive car noise.",
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Patil, HA & Baljekar, PN 2012, Classification of normal and pathological voices using TEO phase and Mel cepstral features. in 2012 International Conference on Signal Processing and Communications, SPCOM 2012., 6289991, 2012 9th International Conference on Signal Processing and Communications, SPCOM 2012, Bangalore, India, 22-07-12. https://doi.org/10.1109/SPCOM.2012.6289991

Classification of normal and pathological voices using TEO phase and Mel cepstral features. / Patil, Hemant A.; Baljekar, Pallavi N.

2012 International Conference on Signal Processing and Communications, SPCOM 2012. 2012. 6289991.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - In this paper, a new feature-set, viz., Teager Energy Operator (TEO) phase has been proposed for automatic classification of normal vs. pathological voices. Development of TEO phase has been motivated from recently proposed linear prediction (LP) residual phase for speaker recognition. Classification was performed using a discriminatively-trained 2 nd order polynomial classifier on a subset of the Massachusetts Ear and Eye Infirmary (MEEI) database. Score-level fusion of TEO phase and state-of-the-art Mel frequency cepstral coefficients (MFCC) gave reduction in equal error rate (EER) by 1.86 % than EER of MFCC alone. Proposed TEO phase feature set is also evaluated under degraded conditions using the NOISEX-92 database for the case of additive car noise.

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Patil HA, Baljekar PN. Classification of normal and pathological voices using TEO phase and Mel cepstral features. In 2012 International Conference on Signal Processing and Communications, SPCOM 2012. 2012. 6289991 https://doi.org/10.1109/SPCOM.2012.6289991