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 proceedingConference contributionpeer-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 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.

Original languageEnglish
Title of host publication2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4607-4610
Number of pages4
ISBN (Electronic)9781424479290
DOIs
Publication statusPublished - 2 Nov 2014
Event2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States
Duration: 26 Aug 201430 Aug 2014

Conference

Conference2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
CountryUnited States
CityChicago
Period26/08/1430/08/14

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