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APPLICATION OF LSTM ARTIFICIAL NEURAL NETWORKS FOR OPERATIONAL ANALYSIS OF ACOUSTIC, MAGNETIC AND VIBRATION FIELDS
Schmidt Institute of Physics of the Earth, Russian Academy of Sciences
Journal: Science and technological developments
Tome: 102
Number: 1
Year: 2023
Pages: 40-64
UDK: 004.32.26+550.34.64
DOI: 10.21455/std2023.1-3
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Gravirov
R.A V.V. APPLICATION OF LSTM ARTIFICIAL NEURAL NETWORKS FOR OPERATIONAL ANALYSIS OF ACOUSTIC, MAGNETIC AND VIBRATION FIELDS // . 2023. Т. 102. № 1. С. 40-64. DOI: 10.21455/std2023.1-3
@article{Gravirov
R.AAPPLICATION2023,
author = "Gravirov
R.A, V. V.",
title = "APPLICATION OF LSTM ARTIFICIAL NEURAL NETWORKS FOR OPERATIONAL ANALYSIS OF ACOUSTIC, MAGNETIC AND VIBRATION FIELDS ",
journal = "Science and technological developments",
year = 2023,
volume = "102",
number = "1",
pages = "40-64",
doi = "10.21455/std2023.1-3 ",
language = "English"
}
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Keywords: deep neural network, data preprocessing, optimization of the neural network structure, acoustic, magnetic and vibration field, laboratory experiment
Аnnotation: In this paper, we discuss the possibility of using a simple artificial neural networks (ANN) for the operational analysis of the physical fields of various sources in a metropolis. The work of ANN with acoustic, vibrational and magnetic signals is considered. Special attention is paid to the optimization of the topology and the main parameters of deep neural networks when working with the considered types of time series of data. The results of the application of ANN for the study of signals from model sources recorded in laboratory conditions and in the presence of urban background are presented.
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Callanan, J., Iqbal, R., Adlakha, R., Behjat, A., Chowdhury, S., Nouh, M., Large-aperture experimental characterization of the acoustic field generated by a hovering unmanned aerial vehicle, J. Acoust. Soc. Amer., 2021, vol. 150, no. 3, pp. 2046–2057. https://doi.org/10.1121/10.0006369
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Costa, R.P., Assael, Y.M., Shillingford, B., de Freitas, N., Vogels, T.P., Cortical microcircuits as gated-recurrent neural networks, Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, December 2017, pp. 271–282. https://doi.org/10.48550/arXiv.1711.02448
Djurek, I., Petosic, A., Grubesa, S., Suhanek, M., Analysis of a quadcopter’s acoustic signature in different flight regimes, IEEE Access, 2020, vol. 8, pp. 10662–10670. https://doi.org/ 10.1109/ACCESS.2020.2965177
Flórez, J., Ortega, J., Betancourt, A., García, A., Bedoya, M., Botero, J.S., A review of algorithms, methods, and techniques for detecting UAVs and UAS using audio, radiofrequency, and video applications, TecnoLógicas, 2020, vol. 23, no. 48, pp. 269–285. https://doi.org/10.22430/ 22565337.1408
Geister, S.R., Small-sized acoustic systems for detecting and measuring the coordinates of firing points, Nauka i voennaya bezopasnost’ (Science and Military Security), 2007, no. 1, pp. 23–27. [in Russian].
Glasberg, B.R., Moore, B.C.J., Derivation of auditory filter shapes from notched-noise data, Hearing Research, 1990, vol. 47, iss. 1–2, pp. 103–138. https://doi.org/10.1016/0378-5955(90)90170-T
Goncharenko, B.I., Kuzmenkov, V.Yu., Kotov, A.N., Experimental study of the peculiarities of the formation of the noise spectrum of the unmanned aircraft, Noise Theory and Practice, 2020, vol. 6, no. 4 (22), pp. 49–59. [in Russian].
Gravirov, V.V., Program for multi-criteria markup of data of physical fields, Certificate of State registration of a computer program, RU 2023618236, 20.04.2023a.
Gravirov, V.V., The program for analyzing the quality of work of LSTM recurrent neural networks, Certificate of State registration of a computer program, RU 2023618085, 18.04.2023b.
Gravirov, V.V., LSTM parameter selection program for recurrent neural networks, Certificate of State registration of a computer program, RU 2023618324, 21.04.2023c.
Gravirov, V.V., Zhostkov, R.A., Presnov, D.A., Acoustic fields of unmanned aerial vehicles in the tasks of passive detection, J. Acoust. Soc. Amer., 2021, vol. 149, no. 4, p. A35. https://doi.org/ 10.1121/10.0004447
Gribachev, V.P., Element base of hardware implementations of neural networks, Komponenty i tekhnologii (Components and technologies), 2006, no. 8. URL: https://kit-e.ru/elcomp/elementnayabaza-apparatnyh-realizaczij-nejronnyh-setej/ [in Russian].
Gwak, D.Y., Han, D., Lee, S., Sound quality factors influencing annoyance from hovering UAV, J. Sound Vibrat., 2020, vol. 489, 16 p. https://doi.org/10.1016/j.jsv.2020.115651
Heutschi, K., Ott, B., Nussbaumer, T., Wellig, P., Synthesis of real world drone signals based on lab recordings, Acta Acust., 2020, vol. 4, no. 6, art. 24, 10 p. https://doi.org/10.1051/aacus/2020023
Hochreiter, S., Schmidhuber, J., Long short-term memory, Neural Comput., 1997, vol. 9, no. 8, pp. 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Kavalchuk, A.N., Petrovsky, A.A., Formula for the transition from the frequency domain to the bark scale and back, Informatika (Informatics), 2011, no. 4 (32), pp. 71–81. [in Russian].
Kloet, N., Watkins, S., Clothier, R., Acoustic signature measurement of small multi-rotor unmanned aircraft systems, Int. J. Micro Air Vehicles, 2017, vol. 9, no. 1, pp. 3–14. https://doi.org/10.1177/1756829316681868
Kotov, A.N., Sobisevich, A.L., Presnov, D.A., Zhostkov, R.A., Study of the spatio-temporal variations of microseismic noise in a megapolis, Geofizika (Geophysics), 2021, no. 2, pp. 82–88. [in Russian].
Luce, R.D., Luce’s choice axiom, Scholarpedia, 2008, vol. 3, no. 12, art. 8077. https://doi.org/
10.4249/scholarpedia.8077
Matson, E.T., Yang, B., Smith, A.H., Dietz, J.E., Gallagher, J.C., UAV detection system with multiple acoustic nodes using machine learning models, Third IEEE International Conference on Robotic Computing (IRC), Napoli, 25–27 February 2019, pp. 493–498. https://doi.org/10.1109/ IRC.2019.00103
Mezei, J., Fiaska, V., Molnár, A., Drone sound detection, 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), Budapest, 19–21 November 2015, pp. 333– 338. https://doi.org/10.1109/CINTI.2015.7382945
Nefyodov, D.S., Puzanov, A.D., Assessment of the detection range of unmanned aerial vehicles by an acoustic system of passive location, Sb. statei 8 Mezhdunarodnoi nauchnoi konferentsii po voenno-tekhnicheskim problemam, problemam oborony i bezopasnosti, ispol’zovaniyu tekhnologii dvoinogo primeneniya (Collection of articles of the 8th International scientific conference on military-technical problems, problems of defense and security, the use of dual-use technologies), Minsk, 16–17 May 2019, Minsk, Laboratoriya intellekta, 2019, pp. 63–66. [in Russian]. https://doi.org/10.31882/978-985-90490-8-8
Qian, Y., Wei, Y., Kong, D., Xu, H., Experimental investigation on motor noise reduction of unmanned aerial vehicles, Appl. Acoust., 2021, vol. 176, art. 107873, 6 p. https://doi.org/ 10.1016/j.apacoust.2020.107873
Rudenko, O.V., Sobisevich, L.E., Sobisevich, A.L., Electromagnetic field of a rotating propeller, Transactions (Doklady) of the Russian Academy of Sciences, Earth Science Sections, 1996, vol. 351, no. 8, pp. 1330–1333.
Sadasivan, S., Gurubasavaraj, M., Ravi Sekar, S., Acoustic signature of an unmanned air vehicle – Exploitation for aircraft localisation and parameter estimation, Def. Sci. J., 2001, vol. 51, no. 3, pp. 279–283. https://doi.org/10.14429/DSJ.51.2238
Shashurin, M.M., Effects of technogenic electromagnetic radiation and fields on living organisms (review), Nauka i obrazovanie (Science and Education), 2015, no. 3 (79), pp. 83–89. [in Russian].
Sinibaldi, G., Marino, L., Experimental analysis on the noise of propellers for small UAV, Appl. Acoust., 2013, vol. 74, no. 1, pp. 79–88. https://doi.org/10.1016/j.apacoust.2012.06.011
Sobyanin, S.S., On fines and special parking for night races, 08.09.2021, URL: http:// www.sobyanin.ru/o-shtrafah-za-nochnye-gonki [in Russian].
Struye, J., Latré, S., Hierarchical temporal memory and recurrent neural networks for time series prediction: An empirical validation and reduction to multilayer perceptrons, Neurocomputing, 2020, vol. 396, pp. 291–301. https://doi.org/10.1016/j.neucom.2018.09.098
Taha, B., Shoufan, A., Machine learning-based drone detection and classification: State-of-the-art in research, IEEE Access, 2019, vol. 7, pp. 138669–138682. https://doi.org/10.1109/ ACCESS.2019.2942944
Tong, J., Hu, Y.-H., Bao, M., Xie, W., Target tracking using acoustic signatures of light-weight aircraft propeller noise, IEEE China Summit and International Conference on Signal and Information Processing, Beijng, 06–10 July 2013, pp. 20–24. https://doi.org/10.1109/ ChinaSIP.2013.6625289
Torija, A.J., Self, R.H., Lawrence, J.L.T., Psychoacoustic characterisation of a small fixed-pitch quadcopter, INTER-NOISE and NOISE-CON Congress and Conference Proceedings, InterNoise19, Madrid, 16–19 June 2019, pp. 1884–1894.
Torija, A.J., Li, Zh., Self, R.H., Effects of a hovering unmanned aerial vehicle on urban soundscapes perception, Transport. Res. D, 2020, vol. 78, art. 102195. https://doi.org/10.1016/ j.trd.2019.11.024
Tyunyaeva, M., Kurasheva, A., “Russian Post” will begin commercial operation of drones in the “next weeks”, Vedomosti, 15.06.2022. URL: http://www.vedomosti.ru/technology/articles/2022/06/ 15/926731-pochta-nachnet-bespilotnikov [in Russian].
Tzinis, E., Wisdom, S., Hershey, J.R., Jansen, A., Ellis, D.P.W., Improving universal sound separation using sound classification, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, 04–08 May 2020, pp. 96–100. https://doi.org/
10.1109/ICASSP40776.2020.9053921
Vorontsov, V.I., Faranosov, G.A., Karabasov, S.A., Zaitsev, M.Yu., Comparison of the noise directivity pattern of the main rotor of a helicopter for flight and hover modes, Acoust. Phys., 2020, vol. 66, no. 3, pp. 303–312. https://doi.org/10.1134/S1063771020030082
Wen, W., Chen, Y., Li, H., He, Y., Rajbhandari, S., Zhang, M., Wang, W., Liu, F. Hu, B., Learning intrinsic sparse structures within long short-term memory, 6th International Conference on Learning Representations, Vancouver, April 30 – May 3, 2018. 11 p. https://doi.org/10.48550/ arXiv.1709.05027
Wulandari, M., Basari, Gunawan, D., Evaluation of wavelet transform preprocessing with deep learning aimed at palm vein recognition application, AIP Conf. Proc., 2019, vol. 2193, iss. 1, art. 050005, 9 p. https://doi.org/10.1063/1.5139378
Zaslavsky, Yu.M., Zaslavsky, V.Yu., Acoustic noise of a low flying quadrocopter, Noise Theory and Practice, 2019, vol. 5, no. 3 (17), pp. 21–27. [in Russian].
Zheng, F., Zhang, G., Song, Zh., Comparison of different implementations of MFCC, J. Comput. Sci. Tech., 2001, vol. 16, iss. 6, pp. 582–589. https://doi.org/10.1007/BF02943243
Zubova, I.I., Batova, E.V., Influence of electromagnetic waves on the human body, Aktual’nye problemy estestvennonauchnogo obrazovaniya, zashchity okruzhayushchei sredy i zdorov’ya cheloveka (Actual problems of natural science education, environmental protection and human health), 2016, no. 2, pp. 147–150. [in Russian].
Zwicker, E., Subdivision of the audible frequency range into critical bands, J. Acoust. Soc. Amer.,
1961, vol. 33, no. 2, p. 248. https://doi.org/10.1121/1.1908630
Aubret, A., Matignon, L., Hassas, S., A survey on intrinsic motivation in reinforcement learning. URL: https://arxiv.org/abs/1908.06976 [Access date: 13.04.2023].
Blanchard, T., Thomas, J.-H., Raoof, K., Acoustic localization and tracking of a multi-rotor unmanned aerial vehicle using an array with few microphones, J. Acoust. Soc. Amer., 2020, vol. 148, no. 3, pp. 1456–1467. https://doi.org/10.1121/10.0001930
Bombizov, A.A., Petrov, A.B., Loshchilov, A.G., Analysis of electromagnetic and acoustic radiation of unmanned aerial vehicles, Doklady TUSUR (Proceedings of TUSUR University), 2018, vol. 21, no. 1, pp. 57–61. [in Russian]. https://doi.org/10.21293/1818-0442-2018-21-1-57-61
Callanan, J., Iqbal, R., Adlakha, R., Behjat, A., Chowdhury, S., Nouh, M., Large-aperture experimental characterization of the acoustic field generated by a hovering unmanned aerial vehicle, J. Acoust. Soc. Amer., 2021, vol. 150, no. 3, pp. 2046–2057. https://doi.org/10.1121/10.0006369
Christian, A.W., Cabell, R., Initial investigation into the psychoacoustic properties of small unmanned aerial system noise, Proceedings of the 23rd AIAA/CEAS Aeroacoustics Conference, Denver, Colorado, 5–9 June 2017, 21 p. https://doi.org/10.2514/6.2017-4051
Costa, R.P., Assael, Y.M., Shillingford, B., de Freitas, N., Vogels, T.P., Cortical microcircuits as gated-recurrent neural networks, Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, December 2017, pp. 271–282. https://doi.org/10.48550/arXiv.1711.02448
Djurek, I., Petosic, A., Grubesa, S., Suhanek, M., Analysis of a quadcopter’s acoustic signature in different flight regimes, IEEE Access, 2020, vol. 8, pp. 10662–10670. https://doi.org/ 10.1109/ACCESS.2020.2965177
Flórez, J., Ortega, J., Betancourt, A., García, A., Bedoya, M., Botero, J.S., A review of algorithms, methods, and techniques for detecting UAVs and UAS using audio, radiofrequency, and video applications, TecnoLógicas, 2020, vol. 23, no. 48, pp. 269–285. https://doi.org/10.22430/ 22565337.1408
Geister, S.R., Small-sized acoustic systems for detecting and measuring the coordinates of firing points, Nauka i voennaya bezopasnost’ (Science and Military Security), 2007, no. 1, pp. 23–27. [in Russian].
Glasberg, B.R., Moore, B.C.J., Derivation of auditory filter shapes from notched-noise data, Hearing Research, 1990, vol. 47, iss. 1–2, pp. 103–138. https://doi.org/10.1016/0378-5955(90)90170-T
Goncharenko, B.I., Kuzmenkov, V.Yu., Kotov, A.N., Experimental study of the peculiarities of the formation of the noise spectrum of the unmanned aircraft, Noise Theory and Practice, 2020, vol. 6, no. 4 (22), pp. 49–59. [in Russian].
Gravirov, V.V., Program for multi-criteria markup of data of physical fields, Certificate of State registration of a computer program, RU 2023618236, 20.04.2023a.
Gravirov, V.V., The program for analyzing the quality of work of LSTM recurrent neural networks, Certificate of State registration of a computer program, RU 2023618085, 18.04.2023b.
Gravirov, V.V., LSTM parameter selection program for recurrent neural networks, Certificate of State registration of a computer program, RU 2023618324, 21.04.2023c.
Gravirov, V.V., Zhostkov, R.A., Presnov, D.A., Acoustic fields of unmanned aerial vehicles in the tasks of passive detection, J. Acoust. Soc. Amer., 2021, vol. 149, no. 4, p. A35. https://doi.org/ 10.1121/10.0004447
Gribachev, V.P., Element base of hardware implementations of neural networks, Komponenty i tekhnologii (Components and technologies), 2006, no. 8. URL: https://kit-e.ru/elcomp/elementnayabaza-apparatnyh-realizaczij-nejronnyh-setej/ [in Russian].
Gwak, D.Y., Han, D., Lee, S., Sound quality factors influencing annoyance from hovering UAV, J. Sound Vibrat., 2020, vol. 489, 16 p. https://doi.org/10.1016/j.jsv.2020.115651
Heutschi, K., Ott, B., Nussbaumer, T., Wellig, P., Synthesis of real world drone signals based on lab recordings, Acta Acust., 2020, vol. 4, no. 6, art. 24, 10 p. https://doi.org/10.1051/aacus/2020023
Hochreiter, S., Schmidhuber, J., Long short-term memory, Neural Comput., 1997, vol. 9, no. 8, pp. 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Kavalchuk, A.N., Petrovsky, A.A., Formula for the transition from the frequency domain to the bark scale and back, Informatika (Informatics), 2011, no. 4 (32), pp. 71–81. [in Russian].
Kloet, N., Watkins, S., Clothier, R., Acoustic signature measurement of small multi-rotor unmanned aircraft systems, Int. J. Micro Air Vehicles, 2017, vol. 9, no. 1, pp. 3–14. https://doi.org/10.1177/1756829316681868
Kotov, A.N., Sobisevich, A.L., Presnov, D.A., Zhostkov, R.A., Study of the spatio-temporal variations of microseismic noise in a megapolis, Geofizika (Geophysics), 2021, no. 2, pp. 82–88. [in Russian].
Luce, R.D., Luce’s choice axiom, Scholarpedia, 2008, vol. 3, no. 12, art. 8077. https://doi.org/
10.4249/scholarpedia.8077
Matson, E.T., Yang, B., Smith, A.H., Dietz, J.E., Gallagher, J.C., UAV detection system with multiple acoustic nodes using machine learning models, Third IEEE International Conference on Robotic Computing (IRC), Napoli, 25–27 February 2019, pp. 493–498. https://doi.org/10.1109/ IRC.2019.00103
Mezei, J., Fiaska, V., Molnár, A., Drone sound detection, 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), Budapest, 19–21 November 2015, pp. 333– 338. https://doi.org/10.1109/CINTI.2015.7382945
Nefyodov, D.S., Puzanov, A.D., Assessment of the detection range of unmanned aerial vehicles by an acoustic system of passive location, Sb. statei 8 Mezhdunarodnoi nauchnoi konferentsii po voenno-tekhnicheskim problemam, problemam oborony i bezopasnosti, ispol’zovaniyu tekhnologii dvoinogo primeneniya (Collection of articles of the 8th International scientific conference on military-technical problems, problems of defense and security, the use of dual-use technologies), Minsk, 16–17 May 2019, Minsk, Laboratoriya intellekta, 2019, pp. 63–66. [in Russian]. https://doi.org/10.31882/978-985-90490-8-8
Qian, Y., Wei, Y., Kong, D., Xu, H., Experimental investigation on motor noise reduction of unmanned aerial vehicles, Appl. Acoust., 2021, vol. 176, art. 107873, 6 p. https://doi.org/ 10.1016/j.apacoust.2020.107873
Rudenko, O.V., Sobisevich, L.E., Sobisevich, A.L., Electromagnetic field of a rotating propeller, Transactions (Doklady) of the Russian Academy of Sciences, Earth Science Sections, 1996, vol. 351, no. 8, pp. 1330–1333.
Sadasivan, S., Gurubasavaraj, M., Ravi Sekar, S., Acoustic signature of an unmanned air vehicle – Exploitation for aircraft localisation and parameter estimation, Def. Sci. J., 2001, vol. 51, no. 3, pp. 279–283. https://doi.org/10.14429/DSJ.51.2238
Shashurin, M.M., Effects of technogenic electromagnetic radiation and fields on living organisms (review), Nauka i obrazovanie (Science and Education), 2015, no. 3 (79), pp. 83–89. [in Russian].
Sinibaldi, G., Marino, L., Experimental analysis on the noise of propellers for small UAV, Appl. Acoust., 2013, vol. 74, no. 1, pp. 79–88. https://doi.org/10.1016/j.apacoust.2012.06.011
Sobyanin, S.S., On fines and special parking for night races, 08.09.2021, URL: http:// www.sobyanin.ru/o-shtrafah-za-nochnye-gonki [in Russian].
Struye, J., Latré, S., Hierarchical temporal memory and recurrent neural networks for time series prediction: An empirical validation and reduction to multilayer perceptrons, Neurocomputing, 2020, vol. 396, pp. 291–301. https://doi.org/10.1016/j.neucom.2018.09.098
Taha, B., Shoufan, A., Machine learning-based drone detection and classification: State-of-the-art in research, IEEE Access, 2019, vol. 7, pp. 138669–138682. https://doi.org/10.1109/ ACCESS.2019.2942944
Tong, J., Hu, Y.-H., Bao, M., Xie, W., Target tracking using acoustic signatures of light-weight aircraft propeller noise, IEEE China Summit and International Conference on Signal and Information Processing, Beijng, 06–10 July 2013, pp. 20–24. https://doi.org/10.1109/ ChinaSIP.2013.6625289
Torija, A.J., Self, R.H., Lawrence, J.L.T., Psychoacoustic characterisation of a small fixed-pitch quadcopter, INTER-NOISE and NOISE-CON Congress and Conference Proceedings, InterNoise19, Madrid, 16–19 June 2019, pp. 1884–1894.
Torija, A.J., Li, Zh., Self, R.H., Effects of a hovering unmanned aerial vehicle on urban soundscapes perception, Transport. Res. D, 2020, vol. 78, art. 102195. https://doi.org/10.1016/ j.trd.2019.11.024
Tyunyaeva, M., Kurasheva, A., “Russian Post” will begin commercial operation of drones in the “next weeks”, Vedomosti, 15.06.2022. URL: http://www.vedomosti.ru/technology/articles/2022/06/ 15/926731-pochta-nachnet-bespilotnikov [in Russian].
Tzinis, E., Wisdom, S., Hershey, J.R., Jansen, A., Ellis, D.P.W., Improving universal sound separation using sound classification, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, 04–08 May 2020, pp. 96–100. https://doi.org/
10.1109/ICASSP40776.2020.9053921
Vorontsov, V.I., Faranosov, G.A., Karabasov, S.A., Zaitsev, M.Yu., Comparison of the noise directivity pattern of the main rotor of a helicopter for flight and hover modes, Acoust. Phys., 2020, vol. 66, no. 3, pp. 303–312. https://doi.org/10.1134/S1063771020030082
Wen, W., Chen, Y., Li, H., He, Y., Rajbhandari, S., Zhang, M., Wang, W., Liu, F. Hu, B., Learning intrinsic sparse structures within long short-term memory, 6th International Conference on Learning Representations, Vancouver, April 30 – May 3, 2018. 11 p. https://doi.org/10.48550/ arXiv.1709.05027
Wulandari, M., Basari, Gunawan, D., Evaluation of wavelet transform preprocessing with deep learning aimed at palm vein recognition application, AIP Conf. Proc., 2019, vol. 2193, iss. 1, art. 050005, 9 p. https://doi.org/10.1063/1.5139378
Zaslavsky, Yu.M., Zaslavsky, V.Yu., Acoustic noise of a low flying quadrocopter, Noise Theory and Practice, 2019, vol. 5, no. 3 (17), pp. 21–27. [in Russian].
Zheng, F., Zhang, G., Song, Zh., Comparison of different implementations of MFCC, J. Comput. Sci. Tech., 2001, vol. 16, iss. 6, pp. 582–589. https://doi.org/10.1007/BF02943243
Zubova, I.I., Batova, E.V., Influence of electromagnetic waves on the human body, Aktual’nye problemy estestvennonauchnogo obrazovaniya, zashchity okruzhayushchei sredy i zdorov’ya cheloveka (Actual problems of natural science education, environmental protection and human health), 2016, no. 2, pp. 147–150. [in Russian].
Zwicker, E., Subdivision of the audible frequency range into critical bands, J. Acoust. Soc. Amer.,
1961, vol. 33, no. 2, p. 248. https://doi.org/10.1121/1.1908630
