Acknowledgement
이 논문은 2023년도 해양수산부 재원으로 해양수산과학기술진흥원의 지원을 받아 수행된 연구임(RS-2023-00259633).
References
- Almeida, L. B., 1994, The fractional Fourier transform and time-frequency representations, IEEE Trans. Signal Process, 42(11), 3084-3091. doi: 10.1109/78.330368
- Au, W. W., and Herzing, D. L., 2003, Echolocation signals of wild Atlantic spotted dolphin (Stenella frontalis), J. Acoust. Soc. Am., 113(1), 598-604. https://doi.org/10.1121/1.1518980
- Caruso, F., Dong, L., Lin, M., Liu, M., Gong, Z.,Xu, W., Alonge, G., and Li, S., 2020, Monitoring of a Nearshore Small Dolphin Species Using Passive Acoustic Platforms and Supervised Machine Learning Techniques, Front. Mar. Sci., 7, 267. https://doi.org/10.3389/fmars.2020.00267
- Cho, K., Van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y., 2014, Learning phrase representations using RNN encoder-decoder for statistical machine translation, arXiv preprint arXiv:1406.1078. https://doi.org/10.48550/arXiv.1406.1078
- Choi, K. H., Yoon, Y. G., Kim, S., Kim, H., Cho, J. W., Bae, H. S., and Park, K., 2019, Analysis of echolocation click signals of Indo-Pacific bottlenose dolphin (Tursiops aduncus) in Jeju Island, J. Acoust. Soc. Korea, 38(1), 56-65 (in Korean with English abstract). https://doi.org/10.7776/ASK.2019.38.1.056
- Cui, Q., Yang, B., Liu, B., Li, Y., and Ning, J., 2022, Tea Category Identification Using Wavelet Signal Reconstruction of Hyperspectral Imagery and Machine Learning, Agriculture, 12(8), 1085. https://doi.org/10.3390/agriculture12081085
- Dey, R., and Salem, F. M., 2017, Gate-variants of gated recurrent unit (GRU) neural networks, In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), 1597-1600. https://doi.org/10.48550/arXiv.1701.05923
- Ditria, E. M., Buelow, C. A., Gonzalez-Rivero, M., and Connolly, R. M., 2022, Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective, Front. Mar. Sci., 9, 918104. https://doi.org/10.3389/fmars.2022.918104
- Frasier, K. E., Roch, M. A., Soldevilla, M. S., Wiggins, S. M., Garrison, L. P., and Hildebrand, J. A., 2017, Automated classification of dolphin echolocation click types from the Gulf of Mexico, PLoS Comput. Biol., 13(12), e1005823. https://doi.org/10.1371/journal.pcbi.1005823
- Graves, A., Mohamed, A. R., and Hinton, G., 2013, Speech recognition with deep recurrent neural networks, In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 6645-6649. doi: 10.1109/ICASSP.2013.6638947
- Hochreiter, S., and Schmidhuber, J., 1997, Long short-term memory, Neural Comput, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
- Jiang, J. J., Bu, L. R., Duan, F. J., Wang, X. Q., Liu, W., Sun, Z. B., and Li, C. Y., 2019, Whistle detection and classification for whales based on convolutional neural networks, Applied Acoustics, 150, 169-178. https://doi.org/10.1016/j.apacoust.2019.02.007
- Jin, C., Kim, M., Jang, S., and Paeng, D. G., 2022, Semantic segmentation-based whistle extraction of indo-pacific bottlenose dolphin residing at the coast of jeju island, Ecol. Indic, 137, 108792. doi: 10.1016/j.ecolind.2022.108792
- Jozefowicz, R., Zaremba, W., and Sutskever, I., 2015, An empirical exploration of recurrent network architectures, In International conference on machine learning, PMLR, 2342-2350. https://proceedings.mlr.press/v37/jozefowicz15.html
- Kavanagh, A. S., Nykanen, M., Hunt, W., Richardson, N., and Jessopp, M. J., 2019, Seismic surveys reduce cetacean sightings across a large marine ecosystem, Scientific Reports, 9(1), 19164. https://doi.org/10.1038/s41598-019-55500-4
- Kim, D., Kim, J. S., and Song, J., 2022a, Cancellation of dolphin sonar clicks in a communication signal based on adaptive time reversal processing, JASA Express Lett, 2(5), 056001. https://doi.org/10.1121/10.0010375
- Kim, J. H., and Kim, J. Y., 2022, Comparative analysis of performance of BI-LSTM and GRU algorithm for predicting the number of Covid-19 confirmed cases, Journal of the Korea Institute of Information and Communication Engineering, 26(2), 187-192 (in Korean with English abstract). https://doi.org/10.6109/jkiice.2022.26.2.187
- Kim, J. S., Yoon, Y. G., Han, D. G., La, H. S., and Choi, J. W., 2022b, Classification of bearded seals signal based on convolutional neural network, J. Acoust. Soc. Korea, 41(2), 235-241 (in Korean with English abstract). https://doi.org/10.7776/ASK.2022.41.2.235
- Namias, V., 1980, The fractional order Fourier transform and its application to quantum mechanics, IMA J. Appl. Math., 25(3), 241-265. https://doi.org/10.1093/imamat/25.3.241
- Ozaktas, H. M., and Kutay, M. A., 2001, The Fractional Fourier Transform, In 2001 European Control Conference (ECC). IEEE, 1477-1483. doi: 10.23919/ECC.2001.7076127
- Radford, C. A., Jeffs, A. G., Tindle, C. T., and Montgomery, J. C., 2008, Temporal patterns in ambient noise of biological origin from a shallow water temperate reef, Oecologia, 156(4), 921-929. doi: 10.1007/s00442-008-1041-y
- Read, J., Pfahringer, B., Holmes, G., and Frank, E., 2011, Classifier chains for multi-label classification, Mach. Learn., 85, 333-359. https://doi.org/10.1007/s10994-011-5256-5
- Saxena, R., and Singh, K, 2005, Fractional Fourier transform: A novel tool for signal processing, J. Indian Inst. Sci., 85(1), 11-26. https://www.researchgate.net/publication/228341636_Fractional_Fourier_transform_A_novel_tool_for_signal_processing
- Schmidhuber, J., 2015, Deep learning in neural networks: An overview, Neural Networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003
- Schuster, M., and Paliwal, K. K., 1997, Bidirectional recurrent neural networks, IEEE Trans. Signal Process, 45(11), 2673-2681. https://ieeexplore.ieee.org/document/650093 https://doi.org/10.1109/78.650093
- Sugimatsu, H., Kojima, J., Ura, T., Bahl, R., and Tomuro, S., 2015, Development of an automatic discrimination method of the bio-sonar clicks of Irrawaddy dolphin (Orcaella brevirostris) from various types of boat noises, OCEANS 2015 - MTS/IEEE Washington, 1-9. doi: 10.23919/OCEANS.2015.7404489
- Thomas, M., Martin, B., Kowarski, K., Gaudet, B., and Matwin, S., 2019, An end-to-end approach for true detection of low frequency marine mammal vocalizations, J. Acoust. Soc. Am., 146(4), 2959. https://doi.org/10.1121/1.5137278
- Tian, L., 2021, Seismic spectral decomposition using short-time fractional Fourier transform spectrograms, J. Appl. Geophys., 192, 104400. https://doi.org/10.1016/j.jappgeo.2021.104400
- Wenz, G. M., 1962, Acoustic ambient noise in the ocean: Spectra and sources, J. Acoust. Soc. Am., 34(12), 1936-1956. https://doi.org/10.1121/1.1909155
- Zhai, M. Y., 2014. Seismic data denoising based on the fractional Fourier transformation, J. Appl. Geophys., 109, 62-70. https://doi.org/10.1016/j.jappgeo.2014.07.012