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

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 Ene...

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Bibliographic Details
Published in:TheScientificWorld 2013-01, Vol.2013 (2013), p.1-8
Main Authors: Salhi, Lotfi, Cherif, Adnane
Format: Article
Language:English
Subjects:
Acoustics
Adult
Aged
Aged, 80 and over
Algorithms
Automatic classification
Classification
Computation
Diagnosis, Computer-Assisted - methods
Discrete cosine transform
Electronic data processing
Energy
Energy spectra
Environmental audits
Feature extraction
Female
Filter banks
Health aspects
Human performance
Humans
Identification
Information processing
International conferences
Male
Mathematical analysis
Methods
Middle Aged
Multilayer perceptrons
Neural networks
Noise
Noise levels
Pathology
Pattern Recognition, Automated - methods
Performance evaluation
Random noise
Reproducibility of Results
Robustness
Sensitivity and Specificity
Signal processing
Sound Spectrography - methods
Speech Recognition Software
Voice
Voice Disorders - diagnosis
Voice Disorders - physiopathology
Voice Quality
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Description
Summary: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.
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2013/435729