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Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems.
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B. M. Rocha, D. Filos, L. Mendes, G. Serbes, S. Ulukaya, Y. P. Kahya, N. Jakovljevic, T. L. Turukalo, I. M. Vogiatzis, E. Perantoni,
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Q. Kong, Y. Cao, T. Iqbal, Y. Wang, W. Wang, and M. D. Plumbley, “Panns: Large-scale pretrained audio neural networks for audio pattern recognition,”
2020
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2021
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2021
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P. N. Soni, S. Shi, P. R. Sriram, A. Y. Ng, and P. Rajpurkar, “Contrastive learning of heart and lung sounds for label-efficient diagnosis,” 2021. [Online]. Available:
2021
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Q. Zhang, J. Zhang, J. Yuan, H. Huang, Y. Zhang, B. Zhang, G. Lv, S. Lin, N. Wang, X. Liu, M. Tang, Y. Wang, H. Ma, L. Liu, S. Yuan, H. Zhou, J. Zhao, Y. Li, Y. Yin, L. Zhao, G. Wang, and Y. Lian, “Sprsound: Open-source sjtu paediatric respiratory sound database,”
2022
Closest in time.
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2021
Cited alongside, same era.
J. Li, J. Yuan, H. Wang, S. Liu, Q. Guo, Y. Ma, Y. Li, L. Zhao, and G. Wang, “Lungattn: advanced lung sound classification using attention mechanism with dual tqwt and triple stft spectrogram,”
2021
Cited alongside, same era.
W. Song, J. Han, and H. Song, “Contrastive embeddind learning method for respiratory sound classification,” in
2021
Cited alongside, same era.
T. Nguyen and F. Pernkopf, “Lung sound classification using co-tuning and stochastic normalization,”
2022
Closest in time.
Z. Wang and Z. Wang, “A domain transfer based data augmentation method for automated respiratory classification,” in
2022
Closest in time.