# RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting

arXiv: https://arxiv.org/abs/2011.00196 · HTML: https://mlsanity.com/paper/2011.00196/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2020.

## Built on by (most cited first, 26 of 26)

- [Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification](https://mlsanity.com/paper/2305.14032/) (2023)
- [Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection](https://mlsanity.com/paper/2110.14019/) (2021)
- [Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification](https://mlsanity.com/paper/2312.09603/) (2023)
- [Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning](https://mlsanity.com/paper/2210.16192/) (2022)
- [Lung Sound Classification Using Co-tuning and Stochastic Normalization](https://mlsanity.com/paper/2108.01991/) (2021)
- [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)
- [Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance](https://mlsanity.com/paper/2311.06480/) (2023)
- [RespLLM: Unifying Audio and Text with Multimodal LLMs for Generalized Respiratory Health Prediction](https://mlsanity.com/paper/2410.05361/) (2024)
- [Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift](https://mlsanity.com/paper/2112.09196/) (2021)
- [Multi-View Spectrogram Transformer for Respiratory Sound Classification](https://mlsanity.com/paper/2311.09655/) (2023)
- [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)
- [Abnormal Respiratory Sound Identification Using Audio-Spectrogram Vision Transformer](https://mlsanity.com/paper/2405.08342/) (2024)
- [StethoLM: Audio Language Model for Cardiopulmonary Analysis Across Clinical Tasks](https://mlsanity.com/paper/2603.00355/) (2026)
- [Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method](https://mlsanity.com/paper/2407.10828/) (2024)
- [PRECISe : Prototype-Reservation for Explainable Classification under Imbalanced and Scarce-Data Settings](https://mlsanity.com/paper/2408.05754/) (2024)
- [CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning](https://mlsanity.com/paper/2502.00734/) (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)
- [PC-MCL: Patient-Consistent Multi-Cycle Learning with multi-label bias correction for respiratory sound classification](https://mlsanity.com/paper/2601.17080/) (2026)
- [Resp-Agent: An Agent-Based System for Multimodal Respiratory Sound Generation and Disease Diagnosis](https://mlsanity.com/paper/2602.15909/) (2026)
- [RAMoEA-QA: Hierarchical Specialization for Robust Respiratory Audio Question Answering](https://mlsanity.com/paper/2603.06542/) (2026)

## Abstract (arXiv)

Auscultation of respiratory sounds is the primary tool for screening and diagnosing lung diseases. Automated analysis, coupled with digital stethoscopes, can play a crucial role in enabling tele-screening of fatal lung diseases. Deep neural networks (DNNs) have shown a lot of promise for such problems, and are an obvious choice. However, DNNs are extremely data hungry, and the largest respiratory dataset ICBHI has only 6898 breathing cycles, which is still small for training a satisfactory DNN model. In this work, RespireNet, we propose a simple CNN-based model, along with a suite of novel techniques -- device specific fine-tuning, concatenation-based augmentation, blank region clipping, and smart padding -- enabling us to efficiently use the small-sized dataset. We perform extensive evaluation on the ICBHI dataset, and improve upon the state-of-the-art results for 4-class classification by 2.2%