Supervised Contrastive Pretrained ResNet with MixUp to Enhance Respiratory Sound Classification on Imbalanced and Limited Dataset

Supervised Contrastive Pretrained ResNet with MixUp to Enhance Respiratory Sound Classification on Imbalanced and Limited Dataset

2023 IEEE Biomedical Circuits and Systems Conference (BioCAS)

10.1109/BioCAS58349.2023.10389029

http://dx.doi.org/10.1109/biocas58349.2023.10389029

Jinhai Hu, Cong Sheng Leow, Shuailin Tao, Wang Ling Goh, Yuan Gao

18 January 2024

Hu, J., Leow, C. S., Tao, S., Goh, W. L., & Gao, Y. (2023, October 19). Supervised Contrastive Pretrained ResNet with MixUp to Enhance Respiratory Sound Classification on Imbalanced and Limited Dataset. 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS). https://doi.org/10.1109/biocas58349.2023.10389029

This paper proposes a strategy of combining multiple techniques to classify paediatric respiratory sound (PRS) from the Open-Source SJTU Paediatric Respiratory Sound Database. Inspired by recent successes in image classification, this work focuses on improving audio classification with limited and imbalanced datasets through Residual Networks (ResNet). These techniques include augmentations applied to audio features, supervised contrastive (SupCon) pretraining, and MixUp. These three techniques helped reduced overfitting due to imbalanced dataset. To further enhance accuracy, pre-processing, and training hyperparameters were optimized through Bayesian Optimization. The proposed strategy achieved over 95% training accuracies for the four tasks (11, 12, 21, and 22) in the IEEE BioCAS 2023 grand challenge. Through this strategy, the four tasks achieved calculated scores of 0.769, 0.632, 0.662 and 0.512 respectively using the test dataset. The total score is 0.729 including 0.1 obtained from the runtime bonus.

Publisher Copyright

This research / project is supported by the A*STAR - Nanosystems at the Edge programme
Grant Reference no. : A18A1b0055

This research / project is supported by the A*STAR - Cyber-Physiochemical Interface programme
Grant Reference no. : A18A1b0045

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1940-9990

Institute of Microelectronics