An ANN-based Method for Detecting Vocal Fold Pathology

There are different algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods, the first and second stages play a critic...

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Bibliographic Details
Published in:International journal of computer applications 2013-01, Vol.62 (7), p.1-4
Main Authors: Majidnezhad, Vahid, Kheidorov, Igor
Format: Article
Language:English
Subjects:
Algorithms
Classification
Diagnosis
Feature extraction
Mathematical analysis
Pathology
Reduction
Tasks
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Summary:There are different algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods, the first and second stages play a critical role in performance and accuracy of the classification system. In this paper we present initial study of feature extraction and feature reduction in the task of vocal fold pathology diagnosis. A new type of feature vector, based on wavelet packet decomposition and Mel-Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also Principal Component Analysis (PCA) is used for feature reduction. An Artificial Neural Network is used as a classifier for evaluating the performance of our proposed method.
ISSN:0975-8887
0975-8887
DOI:10.5120/10089-4722