Earticle

초록

영어

Various voice disorders exist in the world, and many studies on voice pathology detection have been conducted. Voice pathology detection (VPD) has made various advances by medical examination, but it is necessary to apply artificial intelligence (AI) to VPD for quick and convenient diagnosis of suspected patients and efficient use of expert resources. Recently, research to detect it using artificial intelligence has been studied. In the research of biomedical engineering, automatic VPD system by machine learning algorithms and well-established features has become a research hotspot. We used the VOice ICar fEDerico II (VOICED) dataset, which has been widely used in the VPD system. It contains 150 pathological voices and 58 healthy voices, resulting in class imbalance. In this paper, we try to figure out the degree of accuracy improvement by using the oversampling technique and multiple models to automatically detect and classify pathological voices in the class imbalanced dataset.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
A. Feature Extraction
B. Balancing Approaches
C. Voice Pathology Detection
III. EXPERIMENTAL SETUP
A. Dataset Description
B. Feature Extraction
C. Oversampling
D. Classifier
IV. EXPERIMENT RESULTS
V. CONCLUSIONS
REFERENCES

저자

• Sangchun Park [ Department of Electronic Engineering Sogang University Seoul, South Korea ]
• Sanglee Park [ Department of Computer Science and Engineering Sogang University Seoul, South Korea ]
• Minwoo Choi [ Department of Electronic Engineering Sogang University Seoul, South Korea ]
• Ye-In Park [ Department of Electronic Engineering Sogang University Seoul, South Korea ] Corresponding Author

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