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Journals / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi / 2012 / Cilt: 14 - Sayı: 40

COMPUTER BASED VOICE ANALYSIS ON MEDICAL DIAGNOSIS

BİLGİSAYAR TABANLI SES ANALİZİNİN TIBBİ TANIDA KULLANILMASI

ERKAN ZEKİ ENGİN
Mehmet ENGİN
Pages
11–22
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results of the simulation studies, scaled-conjugate gradient algorithm gave the best results.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The change in voice quality is affected by many of voice disorders that coming from pathogical conditions of voice generation organs.
📊
Key Findings & Data:The aim of this study is to help that the clinicians could be diagnosed about voice disorders with non-invasive based analysis.
💡
Conclusion & Impact:According to the results of the simulation studies, scaled-conjugate gradient algorithm gave the best results.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Ses analizi · Akustik parametreler · Dalgacık dönüşümü

Abstract

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.

Özet

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-Goldfarb- Shanno 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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Cite This Article

ENGİN, E. Z., ENGİN, M. (2012). COMPUTER BASED VOICE ANALYSIS ON MEDICAL DIAGNOSIS. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt: 14 - Sayı: 40, 11–22. https://ajindex.com/articles/215684

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