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

Autor:	Bae, Sangmin, Kim, June-Woo, Cho, Won-Yang, Baek, Hyerim, Son, Soyoun, Lee, Byungjo, Ha, Changwan, Tae, Kyongpil, Kim, Sungnyun, Yun, Se-Young
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Machine Learning Computer Science - Sound
Druh dokumentu:	Working Paper
DOI:	10.21437/Interspeech.2023-1426
Popis:	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%. Comment: INTERSPEECH 2023, Code URL: https://github.com/raymin0223/patch-mix_contrastive_learning
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.14032 Zobrazit plný text záznamu View this record from Arxiv