NDLI: Robustness of Auditory Teager Energy Cepstrum Coefficients for Classification of Pathological and Normal Voices in Noisy Environments

Content Provider	PubMed Central
Author	Salhi, Lotfi Cherif, Adnane
Copyright Year	2013
Abstract	This paper focuses on a robust feature extraction algorithm for automatic classification of pathological and normal voices in noisy environments. The proposed algorithm is based on human auditory processing and the nonlinear Teager-Kaiser energy operator. The robust features which labeled Teager Energy Cepstrum Coefficients (TECCs) are computed in three steps. Firstly, each speech signal frame is passed through a Gammatone or Mel scale triangular filter bank. Then, the absolute value of the Teager energy operator of the short-time spectrum is calculated. Finally, the discrete cosine transform of the log-filtered Teager Energy spectrum is applied. This feature is proposed to identify the pathological voices using a developed neural system of multilayer perceptron (MLP). We evaluate the developed method using mixed voice database composed of recorded voice samples from normophonic or dysphonic speakers. In order to show the robustness of the proposed feature in detection of pathological voices at different White Gaussian noise levels, we compare its performance with results for clean environments. The experimental results show that TECCs computed from Gammatone filter bank are more robust in noisy environments than other extracted features, while their performance is practically similar to clean environments.
Related Links	http://dx.doi.org/10.1155/2013/435729
Starting Page	435729
File Format	PDF
ISSN	1537744X
e-ISSN	1537744X
Journal	The Scientific World Journal
Volume Number	2013
Language	English
Publisher	Hindawi Publishing Corporation
Publisher Date	2013-01-01
Access Restriction	Open
Rights Holder	Hindawi Publishing Corporation
Subject Keyword	Biochemistry, Genetics and Molecular Biology(all) Environmental Science(all) Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Medicine Biochemistry, Genetics and Molecular Biology Environmental Science

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