# Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification

arXiv: https://arxiv.org/abs/2312.09603 · HTML: https://mlsanity.com/paper/2312.09603/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting](https://mlsanity.com/paper/2011.00196/) (2020)
- [Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification](https://mlsanity.com/paper/2305.14032/) (2023)
- [Prototype Learning for Interpretable Respiratory Sound Analysis](https://mlsanity.com/paper/2110.03536/) (2021)
- [Lung Sound Classification Using Co-tuning and Stochastic Normalization](https://mlsanity.com/paper/2108.01991/) (2021)
- [Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysis](https://mlsanity.com/paper/2203.16141/) (2022)

## Built on by (most cited first, 16 of 16)

- [RepAugment: Input-Agnostic Representation-Level Augmentation for Respiratory Sound Classification](https://mlsanity.com/paper/2405.02996/) (2024)
- [BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory Sound Classification](https://mlsanity.com/paper/2406.06786/) (2024)
- [Domain Adversarial Training for Mitigating Gender Bias in Speech-based Mental Health Detection](https://mlsanity.com/paper/2505.03359/) (2025)
- [Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis](https://mlsanity.com/paper/2505.06271/) (2025)
- [Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles](https://mlsanity.com/paper/2505.22027/) (2025)
- [Patient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment Losses](https://mlsanity.com/paper/2505.23834/) (2025)
- [Lungmix: A Mixup-Based Strategy for Generalization in Respiratory Sound Classification](https://mlsanity.com/paper/2501.00064/) (2024)
- [Mitigating Stethoscope-Induced Shortcuts in Respiratory Sound Classification under Federated Domain Generalization with Causality-Inspired Interventions](https://mlsanity.com/paper/2605.29862/) (2026)
- [Noise-Agnostic Multitask Whisper Training for Reducing False Alarm Errors in Call-for-Help Detection](https://mlsanity.com/paper/2501.11631/) (2025)
- [Patient Domain Supervised Contrastive Learning for Lung Sound Classification Using Mobile Phone](https://mlsanity.com/paper/2505.23132/) (2025)
- [Improving Deep Learning-based Respiratory Sound Analysis with Frequency Selection and Attention Mechanism](https://mlsanity.com/paper/2507.20052/) (2025)
- [Geometry-Aware Optimization for Respiratory Sound Classification: Enhancing Sensitivity with SAM-Optimized Audio Spectrogram Transformers](https://mlsanity.com/paper/2512.22564/) (2025)
- [Resp-Agent: An Agent-Based System for Multimodal Respiratory Sound Generation and Disease Diagnosis](https://mlsanity.com/paper/2602.15909/) (2026)
- [Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification](https://mlsanity.com/paper/2604.24096/) (2026)
- [Quality Adaptive Angular Margin Learning for Respiratory Sound Classification](https://mlsanity.com/paper/2606.11915/) (2026)
- [Lung-SRAD: Spectral-Aware Regularized Audio DASS with Dual-Axis Patch-Mix Contrastive Learning for Respiratory Sound Classification](https://mlsanity.com/paper/2606.11922/) (2026)

## Abstract (arXiv)

Despite the remarkable advances in deep learning technology, achieving satisfactory performance in lung sound classification remains a challenge due to the scarcity of available data. Moreover, the respiratory sound samples are collected from a variety of electronic stethoscopes, which could potentially introduce biases into the trained models. When a significant distribution shift occurs within the test dataset or in a practical scenario, it can substantially decrease the performance. To tackle this issue, we introduce cross-domain adaptation techniques, which transfer the knowledge from a source domain to a distinct target domain. In particular, by considering different stethoscope types as individual domains, we propose a novel stethoscope-guided supervised contrastive learning approach. This method can mitigate any domain-related disparities and thus enables the model to distinguish respiratory sounds of the recording variation of the stethoscope. The experimental results on the ICBHI dataset demonstrate that the proposed methods are effective in reducing the domain dependency and achieving the ICBHI Score of 61.71%, which is a significant improvement of 2.16% over the baseline.