Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification
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%.
Cite
@article{arxiv.2305.14032,
title = {Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification},
author = {Sangmin Bae and June-Woo Kim and Won-Yang Cho and Hyerim Baek and Soyoun Son and Byungjo Lee and Changwan Ha and Kyongpil Tae and Sungnyun Kim and Se-Young Yun},
journal= {arXiv preprint arXiv:2305.14032},
year = {2024}
}
Comments
INTERSPEECH 2023, Code URL: https://github.com/raymin0223/patch-mix_contrastive_learning