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.
Язык оригиналаанглийский
Название основной публикацииLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Подзаголовок основной публикации13th International Symposium on Neural Networks, ISNN 2016; St. Petersburg; Russian Federation; 6 July 2016 through 8 July 2016; Code 177689
ИздательSpringer Nature
Страницы108-114
Том9719
ISBN (печатное издание)978-331940662-6
DOI
СостояниеОпубликовано - 2016
Опубликовано для внешнего пользованияДа
СобытиеAdvances in Neural Networks: 13th International Symposium on Neural Networks - St. Petersburg, Российская Федерация
Продолжительность: 6 июн 20168 июн 2016

конференция

конференцияAdvances in Neural Networks
Сокращенное названиеISNN 2016
Страна/TерриторияРоссийская Федерация
ГородSt. Petersburg
Период6/06/168/06/16

ID: 8625443