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

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Cybernetics and Systems Analysis Aims and scope

Abstract

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.

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Author information

Authors and Affiliations

  1. Silentium Systems, Vancouver, Canada

    V. I. Solovyov

  2. National Academy of Internal Affairs, Kyiv, Ukraine

    O. V. Rybalskiy

  3. Kyiv Expert and Forensic Center of the Ministry of Internal Affairs of Ukraine, Kyiv, Ukraine

    V. V. Zhuravel

  4. Odessa Scientific Research Institute of Forensic Expertise of the Ministry of Justice of Ukraine, Odessa, Ukraine

    A. N. Shablya

  5. Kyiv Scientific Research Institute of Forensic Expertise of the Ministry of Justice of Ukraine, Kyiv, Ukraine

    E. V. Tymko

Authors
  1. V. I. Solovyov
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  2. O. V. Rybalskiy
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  3. V. V. Zhuravel
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  4. A. N. Shablya
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  5. E. V. Tymko
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Corresponding author

Correspondence to V. I. Solovyov.

Additional information

Translated from Kibernetyka ta Systemnyi Analiz, No. 1, January–February, 2022, pp. 11–20.

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Solovyov, V.I., Rybalskiy, O.V., Zhuravel, V.V. et al. Information Redundancy in Constructing Systems for Audio Signal Examination on Deep Learning Neural Networks. Cybern Syst Anal 58, 8–15 (2022). https://doi.org/10.1007/s10559-022-00429-2

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  • DOI: https://doi.org/10.1007/s10559-022-00429-2

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