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

Autor:	Solovyov, V. I., Rybalskiy, O. V., Zhuravel, V. V., Shablya, A. N., Tymko, E. V.
Předmět:	DEEP learning SOUND systems SIGNAL processing
Zdroj:	Cybernetics & Systems Analysis; Jan2022, Vol. 58 Issue 1, p8-15, 8p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Full text from SpringerLink