Advanced Time-Frequency Analysis and Machine Learning for Pathological Voice Detection

被引：0

作者：

Georgopoulos, Voula C. ^{[1
]}

机构：

[1] Univ Patras, Dept Speech & Language Therapy, Patras, Greece

来源：

2020 12TH INTERNATIONAL SYMPOSIUM ON COMMUNICATION SYSTEMS, NETWORKS AND DIGITAL SIGNAL PROCESSING, CSNDSP | 2020年

关键词：

time-frequency analysis; machine learning; voice disorders; WIGNER DISTRIBUTION; WAVELET-ANALYSIS; NEURAL-NETWORKS; QUALITY; SIGNALS;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper discusses an advanced positive time-frequency analysis method with accurate zero-, first- and second-order moments combined with machine learning for detection of pathological voice signals. The time-frequency analysis is based on the Wigner-Ville distribution. The transfer learning approach is used to train a convolutional neural network. Specifically, the GoogLeNet network is trained using the normal and pathological voices of the KAY database and the results are presented.

引用

页数：5

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