A method for calculating the largest Lyapunov exponents analogs for the numerical series obtained from acoustic experimental data is proposed. It is based on the use of artificial neural networks for constructing special additional series which are necessary in the process of calculating the Lyapunov exponents. The musical compositions have been used as acoustic data. It turned out that the error of the largest Lyapunov exponent computations within a single musical composition is sufficiently small. On the other hand for the compositions with different acoustic content there were obtained various numerical values Lyapunov exponents. This enables to make conclusion that the proposed procedure for calculating the Lyapunov exponents is adequate. It also allows to use the obtained results as an additional macroscopic characteristics of acoustic data for comparative analysis.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Subtitle of host publication13th International Symposium on Neural Networks, ISNN 2016; St. Petersburg; Russian Federation; 6 July 2016 through 8 July 2016; Code 177689
PublisherSpringer Nature
Pages108-114
Volume9719
ISBN (Print)978-331940662-6
DOIs
StatePublished - 2016
Externally publishedYes
EventAdvances in Neural Networks: 13th International Symposium on Neural Networks - St. Petersburg, Russian Federation
Duration: 6 Jun 20168 Jun 2016

Conference

ConferenceAdvances in Neural Networks
Abbreviated titleISNN 2016
Country/TerritoryRussian Federation
CitySt. Petersburg
Period6/06/168/06/16

ID: 8625443