Abstract—The problem of binary classification of acoustic signals of biological origin recorded in the real world is considered. Information features such as entropy and statistical complexity are chosen as the characteristic description of objects. The solution methods are based on three neural network architectures modified by the authors (on the Inception core, on the Inception core with the Residual technology, and on the Self-Attention structure with LSTM blocks). A dataset from the Kaggle competition for detecting acoustic signatures of whales was used, and the models were compared in terms of the quality of solving the problem involved on a standard set of metrics. The AUC ROC value of more than 90% was obtained, which means that the problem of detecting a useful signal is solved successfully and indicates that information features can be applied to similar tasks.
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REFERENCES
C. M. Bishop, Pattern Recognition and Machine Learning (Springer, Berlin, 2006). https://link.springer.com/book/9780387310732
F. Karim, S. Majumdar, H. Darabi, and S. Chen, “LSTM fully convolutional networks for time series classification,” IEEE Access 6, 1662–1669 (2018). http://ieeexplore.ieee.org/document/8141873
Q. Xiao, K. Lee, S. A. Mokhtar, I. Ismail, A. L. b. M. Pauzi, Q. Zhang, and P. Y. Lim, “Deep learning-based ECG arrhythmia classification: A systematic review,” Appl. Sci. 13 (8), 4964 (2023). https://doi.org/10.3390/app13084964
T. D. Pham, “Time-frequency time-space LSTM for robust classification of physiological signals,” Sci. Rep. 11 (1), 6936 (2021). https://doi.org/10.1038/s41598-021-86432-7
G. Kłosowski, T. Rymarczyk, D. Wójcik, et al., “The use of time-frequency moments as inputs of LSTM network for ECG signal classification,” Electronics 9 (9), 1452 (2020). https://doi.org/10.3390/electronics9091452
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778.
O. S. Kirsebom, F. Frazao, Y. Simard, et al., “Performance of a deep neural network at detecting North Atlantic right whale upcalls,” J. Acoust. Soc. Am. 147 (4), 2636–2646 (2020). https://doi.org/10.1121/10.0001132
H. I. Fawaz, B. Lucas, G. Forestier, et al., “InceptionTime: Finding AlexNet for time series classification,” Data Min. Knowl. Discovery 34 (6), 1936–1962 (2020). https://link.springer.com/10.1007/s10618-020-00710-y
B. Zhao, H. Lu, S. Chen, et al., “Convolutional neural networks for time series classification,” J. Syst. Eng. Electron. 28 (1), 162–169 (2017). http://ieeexplore.ieee.org/document/7870510
A. Vaswani, N. Shazeer, N. Parmar, et al., “Attention is all you need,” Advances in Neural Information Processing Systems (2017), Vol. 30. https://arxiv.org/abs/1706.03762
G. Liu and J. Guo, “Bidirectional LSTM with attention mechanism and convolutional layer for text classification,” Neurocomputing 337, 325–338 (2019). www.sciencedirect.com/science/article/pii/S0925231219301067
V.-S. Doan, T. Huynh-The, and D.-S. Kim, “Underwater acoustic target classification based on dense convolutional neural network,” IEEE Geosci. Remote Sensing Lett. 19, 1500905 (2022). https://ieeexplore.ieee.org/document/9229102
P. C. Bermant, M. M. Bronstein, R. J. Wood, et al., “Deep machine learning techniques for the detection and classification of sperm whale bioacoustics,” Sci. Rep. 9 (1), 12588 (2019). www.nature.com/articles/s41598-019-48909-4
J. Jiang, Z. Wu, J. Lu, et al., “Interpretable features for underwater acoustic target recognition,” Measurement 173, 108586 (2021). https://doi.org/10.1016/j.measurement.2020.108586
L. Berlin, A. Galyaev, and P. Lysenko, “Comparison of information criteria for detection of useful signals in noisy environments,” Sensors 23 (4), 2133 (2023). https://doi.org/10.3390/s23042133
L. Berlin, A. Galyaev, and P. Lysenko, “Statistical complexity as a criterion for the useful signal detection problem,” Autom. Remote Control 84 (7), 858–871 (2023). http://ait.mtas.ru/en/archive/volume84issue7/AutRemControl2023-07-07Galyaev.pdf
J. Wang and Z. Chen, “Feature extraction of ship-radiated noise based on intrinsic time-scale decomposition and a statistical complexity measure,” Entropy 21 (11), 1079 (2019). www.mdpi.com/1099-4300/21/11/1079
S. Siddagangaiah, Y. Li, X. Guo, et al., “A complexity-based approach for the detection of weak signals in ocean ambient noise,” Entropy 18 (3), 101 (2016). www.mdpi.com/1099-4300/18/3/101
The Marinexplore and Cornell University Whale Detection Challenge (2013). www.kaggle.com/competitions/whale-detection-challenge/overview
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The study was supported by the Russian Science Foundation (grant 23-19-00134).
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Lysenko, P.V., Nasonov, I.A., Galyaev, A.A. et al. Deep Learning Approach to Classification of Acoustic Signals Using Information Features. Dokl. Math. 108 (Suppl 2), S196–S204 (2023). https://doi.org/10.1134/S1064562423701065
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DOI: https://doi.org/10.1134/S1064562423701065