# Lung Sound Classification Using Co-tuning and Stochastic Normalization

arXiv: https://arxiv.org/abs/2108.01991 · HTML: https://mlsanity.com/paper/2108.01991/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 58 references that are in the map.
- First posted on arXiv 4 Aug 2021.

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

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Rethinking ImageNet Pre-training](https://mlsanity.com/paper/1811.08883/) (2018)
- [The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms](https://mlsanity.com/paper/2009.11644/) (2020)
- [COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings](https://mlsanity.com/paper/2012.01926/) (2020)
- [RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting](https://mlsanity.com/paper/2011.00196/) (2020)
- [Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks](https://mlsanity.com/paper/1907.05708/) (2019)
- [A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogram](https://mlsanity.com/paper/2009.04402/) (2020)
- [Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases](https://mlsanity.com/paper/2002.03894/) (2020)
- [Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning](https://mlsanity.com/paper/2004.08287/) (2020)
- [Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolitional Neural Networks](https://mlsanity.com/paper/2104.14921/) (2021)

## Built on by (most cited first, 14 of 14)

- [Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification](https://mlsanity.com/paper/2305.14032/) (2023)
- [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)
- [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)
- [Multi-View Spectrogram Transformer for Respiratory Sound Classification](https://mlsanity.com/paper/2311.09655/) (2023)
- [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)
- [An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies](https://mlsanity.com/paper/2303.04104/) (2023)
- [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)
- [Spectrogram features for audio and speech analysis](https://mlsanity.com/paper/2603.14917/) (2026)
- [Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification](https://mlsanity.com/paper/2604.24096/) (2026)

## Abstract (arXiv)

In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. The knowledge of the pre-trained model is transferred by using vanilla fine-tuning, co-tuning, stochastic normalization and the combination of the co-tuning and stochastic normalization techniques. Furthermore, data augmentation in both time domain and time-frequency domain is used to account for the class imbalance of the ICBHI and our multi-channel lung sound dataset. Additionally, we apply spectrum correction to consider the variations of the recording device properties on the ICBHI dataset. Empirically, our proposed systems mostly outperform all state-of-the-art lung sound classification systems for the adventitious lung sounds and respiratory diseases of both datasets.