Deep Learning Approach to Classification of Acoustic Signals Using Information Features-Reference-Cited by-同舟云学术

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Deep Learning Approach to Classification of Acoustic Signals Using Information Features

Published:2023-12 Issue:S2 Volume:108 Page:S196-S204
ISSN:1064-5624
Container-title:Doklady Mathematics
language:en
Short-container-title:Dokl. Math.

Author:

Lysenko P. V.,Nasonov I. A.,Galyaev A. A.,Berlin L. M.

Publisher

Pleiades Publishing Ltd

Link

https://link.springer.com/content/pdf/10.1134/S1064562423701065.pdf

Reference19 articles.

1. C. M. Bishop, Pattern Recognition and Machine Learning (Springer, Berlin, 2006). https://link.springer.com/book/9780387310732

2. 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

3. 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

4. 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

5. 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

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1. Solving Inverse Problems of Obtaining Super-Resolution Using Neural Networks;Bulletin of the South Ural State University. Series "Mathematical Modelling, Programming and Computer Software";2024

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