mlsanityData to 7 Oct 2026

Lung Sound Classification Using Co-tuning and Stochastic Normalization

arXiv 2108.01991 ↗First posted 4 Aug 2021eess.AS cs.LG cs.SDcited by 14

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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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 10 of its 58 references that are in the map. First posted on arXiv 4 Aug 2021.

Builds on

10 of 58 references.

References · 10

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2018Rethinking ImageNet Pre-trainingcited by 376
  3. 2020The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithmscited by 59
  4. 2020COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordingscited by 36
  5. 2020RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Settingcited by 26
  6. 2019Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networkscited by 13
  7. 2020A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogramcited by 13
  8. 2020Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseasescited by 11
  9. 2020Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuningcited by 11
  10. 2021Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolitional Neural Networkscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 1; 2025: 4; 2026: 3.

14 papers.

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  1. 2023Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classificationcited by 24
  2. 2023Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classificationcited by 16
  3. 2022Pretraining Respiratory Sound Representations using Metadata and Contrastive Learningcited by 15
  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. 2025Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensemblescited by 3
  8. 2025Patient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment Lossescited by 3
  9. 2023An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomaliescited by 2
  10. 2025Improving Deep Learning-based Respiratory Sound Analysis with Frequency Selection and Attention Mechanismcited by 0
  11. 2025Geometry-Aware Optimization for Respiratory Sound Classification: Enhancing Sensitivity with SAM-Optimized Audio Spectrogram Transformerscited by 0
  12. 2026Resp-Agent: An Agent-Based System for Multimodal Respiratory Sound Generation and Disease Diagnosiscited by 0
2 more
  1. 2026Spectrogram features for audio and speech analysiscited by 0
  2. 2026Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classificationcited by 0