mlsanityData to 7 Oct 2026

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

arXiv 2305.14032 ↗First posted 23 May 2023eess.AS cs.LG cs.SDcited by 24

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Abstract (arXiv)

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%.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 15 of its 36 references that are in the map. First posted on arXiv 23 May 2023.

Builds on

15 of 36 references.

References · 15

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  4. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  5. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
  6. 2021AST: Audio Spectrogram Transformercited by 634
  7. 2021SSAST: Self-Supervised Audio Spectrogram Transformercited by 166
  8. 2022MAE-AST: Masked Autoencoding Audio Spectrogram Transformercited by 82
  9. 2020MixCo: Mix-up Contrastive Learning for Visual Representationcited by 62
  10. 2022TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformerscited by 48
  11. 2020RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Settingcited by 26
  12. 2022TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformerscited by 26
3 more
  1. 2021Prototype Learning for Interpretable Respiratory Sound Analysiscited by 21
  2. 2021Lung Sound Classification Using Co-tuning and Stochastic Normalizationcited by 14
  3. 2022Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysiscited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 9; 2026: 7.

24 papers.

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  1. 2024Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarkingcited by 23
  2. 2023Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classificationcited by 16
  3. 2024RepAugment: Input-Agnostic Representation-Level Augmentation for Respiratory Sound Classificationcited by 12
  4. 2024BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory Sound Classificationcited by 12
  5. 2023Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalancecited by 11
  6. 2023Multi-View Spectrogram Transformer for Respiratory Sound Classificationcited by 3
  7. 2025Domain Adversarial Training for Mitigating Gender Bias in Speech-based Mental Health Detectioncited by 3
  8. 2025Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosiscited by 3
  9. 2025Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensemblescited by 3
  10. 2025Patient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment Lossescited by 3
  11. 2024Lungmix: A Mixup-Based Strategy for Generalization in Respiratory Sound Classificationcited by 2
  12. 2026Synthetic Data Augmentation for Medical Audio Classification: A Preliminary Evaluationcited by 1
12 more
  1. 2026Mitigating Stethoscope-Induced Shortcuts in Respiratory Sound Classification under Federated Domain Generalization with Causality-Inspired Interventionscited by 1
  2. 2024Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Methodcited by 0
  3. 2025iMedic: Towards Smartphone-based Self-Auscultation Tool for AI-Powered Pediatric Respiratory Assessmentcited by 0
  4. 2025Patient Domain Supervised Contrastive Learning for Lung Sound Classification Using Mobile Phonecited by 0
  5. 2025Improving Deep Learning-based Respiratory Sound Analysis with Frequency Selection and Attention Mechanismcited by 0
  6. 2025EZhouNet:A framework based on graph neural network and anchor interval for the respiratory sound event detectioncited by 0
  7. 2025Geometry-Aware Optimization for Respiratory Sound Classification: Enhancing Sensitivity with SAM-Optimized Audio Spectrogram Transformerscited by 0
  8. 2026PC-MCL: Patient-Consistent Multi-Cycle Learning with multi-label bias correction for respiratory sound classificationcited by 0
  9. 2026Resp-Agent: An Agent-Based System for Multimodal Respiratory Sound Generation and Disease Diagnosiscited by 0
  10. 2026Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classificationcited by 0
  11. 2026Quality Adaptive Angular Margin Learning for Respiratory Sound Classificationcited by 0
  12. 2026Lung-SRAD: Spectral-Aware Regularized Audio DASS with Dual-Axis Patch-Mix Contrastive Learning for Respiratory Sound Classificationcited by 0