Spectral envelope and periodic component in classification trees for pathological voice diagnostic

H. Cordeiro; J. Fonseca; C. Meneses

doi:10.1109/EMBC.2014.6944650

Spectral envelope and periodic component in classification trees for pathological voice diagnostic

H. Cordeiro, J. Fonseca, C. Meneses

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

This work investigates the effectiveness of features from the spectral envelope such as the frequency and bandwidth of the first peak obtained from a 30^th order Linear Predictive Coefficients (LPC) to identify pathological voices. Other spectral features are also investigated and tested to improve the recognition rate. The value of the Relative Power of the Periodic Component is combined with spectral features, to diagnose pathological voices. Healthy voices and five vocal folds pathologies are tested. Decision Tree classifiers are used to evaluate which features have pathological voice information. Based on those results a simple Decision Tree was implemented and 94% of all the subjects in the database are correctly diagnosed.

Original language	English
Title of host publication	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4607-4610
Number of pages	4
ISBN (Electronic)	9781424479290
DOIs	https://doi.org/10.1109/EMBC.2014.6944650 https://doi.org/10.1109/EMBC.2014.6944650
Publication status	Published - 2 Nov 2014
Event	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States Duration: 26 Aug 2014 → 30 Aug 2014

Conference

Conference	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Country/Territory	United States
City	Chicago
Period	26/08/14 → 30/08/14

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Cordeiro, H., Fonseca, J., & Meneses, C. (2014). Spectral envelope and periodic component in classification trees for pathological voice diagnostic. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 (pp. 4607-4610). [6944650] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2014.6944650, https://doi.org/10.1109/EMBC.2014.6944650

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title = "Spectral envelope and periodic component in classification trees for pathological voice diagnostic",

abstract = "This work investigates the effectiveness of features from the spectral envelope such as the frequency and bandwidth of the first peak obtained from a 30th order Linear Predictive Coefficients (LPC) to identify pathological voices. Other spectral features are also investigated and tested to improve the recognition rate. The value of the Relative Power of the Periodic Component is combined with spectral features, to diagnose pathological voices. Healthy voices and five vocal folds pathologies are tested. Decision Tree classifiers are used to evaluate which features have pathological voice information. Based on those results a simple Decision Tree was implemented and 94% of all the subjects in the database are correctly diagnosed.",

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Cordeiro, H, Fonseca, J & Meneses, C 2014, Spectral envelope and periodic component in classification trees for pathological voice diagnostic. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014., 6944650, Institute of Electrical and Electronics Engineers Inc., pp. 4607-4610, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, United States, 26/08/14. https://doi.org/10.1109/EMBC.2014.6944650, https://doi.org/10.1109/EMBC.2014.6944650

Spectral envelope and periodic component in classification trees for pathological voice diagnostic. / Cordeiro, H.; Fonseca, J.; Meneses, C.

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 4607-4610 6944650.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Meneses, C.

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N2 - This work investigates the effectiveness of features from the spectral envelope such as the frequency and bandwidth of the first peak obtained from a 30th order Linear Predictive Coefficients (LPC) to identify pathological voices. Other spectral features are also investigated and tested to improve the recognition rate. The value of the Relative Power of the Periodic Component is combined with spectral features, to diagnose pathological voices. Healthy voices and five vocal folds pathologies are tested. Decision Tree classifiers are used to evaluate which features have pathological voice information. Based on those results a simple Decision Tree was implemented and 94% of all the subjects in the database are correctly diagnosed.

AB - This work investigates the effectiveness of features from the spectral envelope such as the frequency and bandwidth of the first peak obtained from a 30th order Linear Predictive Coefficients (LPC) to identify pathological voices. Other spectral features are also investigated and tested to improve the recognition rate. The value of the Relative Power of the Periodic Component is combined with spectral features, to diagnose pathological voices. Healthy voices and five vocal folds pathologies are tested. Decision Tree classifiers are used to evaluate which features have pathological voice information. Based on those results a simple Decision Tree was implemented and 94% of all the subjects in the database are correctly diagnosed.

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Cordeiro H, Fonseca J, Meneses C. Spectral envelope and periodic component in classification trees for pathological voice diagnostic. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 4607-4610. 6944650 https://doi.org/10.1109/EMBC.2014.6944650, https://doi.org/10.1109/EMBC.2014.6944650

Spectral envelope and periodic component in classification trees for pathological voice diagnostic

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