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BİLGİSAYAR TABANLI SES ANALİZİNİN TIBBİ TANIDA KULLANILMASI

COMPUTER BASED VOICE ANALYSIS ON MEDICAL DIAGNOSIS

Journal Name:

  • Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi

Publication Year:

  • 2012

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
The change in voice quality is affected by many of voice disorders that coming from pathogical conditions of voice generation organs. The aim of this study is to help that the clinicians could be diagnosed about voice disorders with non-invasive based analysis. In our work, amplitude perturbation quotient, pitch period perturbation quotient, degree of unvoiceness, Teager Energy Operators averages of wavelet transform coefficients, and higher-order statistics parameters have formed the feature vectors. The voice segments belonging to different pathological or normal classes were classified by backpropagation based multilayer perceptron networks. In backpropagation based multilayer perceptron networks, resilient, scaled-conjugate gradient, and Brodyen-Fletcher-GoldfarbShanno learning algorithms were used in training. According to the results of the simulation studies, scaled-conjugate gradient algorithm gave the best results.
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Abstract (Original Language): 
Sesin oluşmasını sağlayan organlarındaki patolojik durumlardan kaynaklanan ses hastalıklarının birçoğu sesin kalitesinde değişime sebep olur. Uzmanlar, sesteki hastalıklara tanı koymak için değişik yöntemler kullanmaktadır. Bu çalışmada; örselemesiz tabanlı analiz ile, doktorun tanı koymasına yardımcı olunmaktadır. Genlik değişim oranı, perde değişim oranı, sessizlik derecesi, Teager enerji ortalamalı dalgacık dönüşüm katsayıları ve yüksek dereceli istatistik parametreleri ile öznitelik vektörleri oluşturulmuştur. Sağlıklı veya farklı hastalık sınıflarına ait ses bölütleri, geriye yayınım temelli çok katmanlı algılayıcı ağlar ile sınıflandırılmıştır. Geriye yayınım temelli ağlar; esnek, ölçekli-eşlenik gradyan ve Brodyen-Fletcher-Goldfarb-Shanno (BFGS) öğrenme algoritmaları ile eğitilmiştir. Benzetim çalışmaları sonucunda, ölçekli-eşlenik gradyan algoritmasının en iyi sonucu verdiği bulunmuştur.
FULL TEXT (PDF): 
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  • 1
11-22
  • Turkish

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