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Calculation of Analogs for the Largest Lyapunov Exponents for Acoustic Data by Means of Artificial Neural Networks

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Advances in Neural Networks – ISNN 2016 (ISNN 2016)

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Abstract

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.

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Authors and Affiliations

  1. Saint Petersburg State University, Universitetskaya Nab. 7/9, 199034, Saint Petersburg, Russia

    German A. Chernykh, Yuri A. Kuperin & Ludmila A. Dmitrieva

  2. Saint-Petersburg State Polytechnic University, Grazhdanskii Pros., 28, 195220, Saint Petersburg, Russia

    Angelina A. Navleva

Authors
  1. German A. Chernykh
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  2. Yuri A. Kuperin
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  3. Ludmila A. Dmitrieva
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  4. Angelina A. Navleva
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Corresponding author

Correspondence to Ludmila A. Dmitrieva .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. Huazhong University of Science and Tech., Wuhan, Jiangsu, China

    Qingshan Liu

  3. Russian Academy of Sciences, SPIIRAS, St. Petersburg, Russia

    Andrey Ronzhin

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Chernykh, G.A., Kuperin, Y.A., Dmitrieva, L.A., Navleva, A.A. (2016). Calculation of Analogs for the Largest Lyapunov Exponents for Acoustic Data by Means of Artificial Neural Networks. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_13

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  • DOI: https://doi.org/10.1007/978-3-319-40663-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40662-6

  • Online ISBN: 978-3-319-40663-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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