Earticle

초록

영어

Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are different approaches and 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 (Support Vector Machines, Artificial Neural Networks, etc), 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 a new SVM-Based method for feature reduction stage is proposed and compared with conventional methods such as Principal Component Analysis (PCA). Support vector machine is used as a classifier for evaluating the performance of the proposed method. The results show the priority of the proposed method in comparison with current methods.

목차

Abstract
 1. Introduction
 2. Methodology
  2.1. Feature Extraction
  2.2. Feature Reduction
  2.3. Support Vector Machines
 3. Experiments and Results
  3.1. Dataset Description
  3.2. Results
  3.3. Discussion
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Vahid Majidnezhad [ Department of Computer Engineering, Shabestar Branch, Islamic Azad University ]
• Igor Kheidorov [ Department of Computer Engineering, Shabestar Branch, Islamic Azad University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605