Information Redundancy in Constructing Systems for Audio Signal Examination on Deep Learning Neural Networks

被引：0
|
作者：
V. I. Solovyov
O. V. Rybalskiy
V. V. Zhuravel
A. N. Shablya
E. V. Tymko
机构：
[1] Silentium Systems,
[2] National Academy of Internal Affairs,undefined
[3] Kyiv Expert and Forensic Center of the Ministry of Internal Affairs of Ukraine,undefined
[4] Odessa Scientific Research Institute of Forensic Expertise of the Ministry of Justice of Ukraine,undefined
[5] Kyiv Scientific Research Institute of Forensic Expertise of the Ministry of Justice of Ukraine,undefined
来源：
Cybernetics and Systems Analysis | 2022年 / 58卷
关键词：
Morlet wavelet; time window; time-frequency transformation; speaker; identification; redundancy; neural network; spectrum; phonogram;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Preliminary signal processing methods used to create new tools to examine materials and digital sound recording means are described. It is shown that using information redundancy when creating a training base for deep learning neural networks used for such examination increases speaker identification efficiency based on voice characteristic parameters by about 15%. It is shown that the proposed processing methods enable speaker identification based on phonograms that are 1 second long.
引用
收藏
页码：8 / 15
页数：7
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