Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification

Sangmin Bae; June-Woo Kim; Won-Yang Cho; Hyerim Baek; Soyoun Son; Byungjo Lee; Changwan Ha; Kyongpil Tae; Sungnyun Kim; Se-Young Yun

doi:10.21437/interspeech.2023-1426

arXiv · 2305.14032

Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification

Sangmin Bae·June-Woo Kim·Won-Yang Cho·Hyerim Baek·Soyoun Son·Byungjo Lee·Changwan Ha·Kyongpil Tae·Sungnyun Kim·Se-Young Yun

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Abstract

Respiratory sound contains crucial information for the early diagnosis of fatal lung diseases. Since the COVID-19 pandemic, there has been a growing interest in contact-free medical care based on electronic stethoscopes. To this end, cutting-edge deep learning models have been developed to diagnose lung diseases; however, it is still challenging due to the scarcity of medical data. In this study, we demonstrate that the pretrained model on large-scale visual and audio datasets can be generalized to the respiratory sound classification task. In addition, we introduce a straightforward Patch-Mix augmentation, which randomly mixes patches between different samples, with Audio Spectrogram Transformer (AST). We further propose a novel and effective Patch-Mix Contrastive Learning to distinguish the mixed representations in the latent space. Our method achieves state-of-the-art performance on the ICBHI dataset, outperforming the prior leading score by an improvement of 4.08%.

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BibTeX RIS

Sangmin Bae, June-Woo Kim, Won-Yang Cho, Hyerim Baek, Soyoun Son, Byungjo Lee, Changwan Ha, Kyongpil Tae, Sungnyun Kim, Se-Young Yun. 2023-05-23. Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification. https://doi.org/10.21437/interspeech.2023-1426

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