Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning

Author:

Moummad Ilyass1,Farrugia Nicolas1

Affiliation:

1. IMT Atlantique,Lab-STICC, UMR CNRS 6285,Brest,France,F-29238

Publisher

IEEE

Reference20 articles.

1. Contrastive Embeddind Learning Method for Respiratory Sound Classification

2. Lung Sound Classification Using Co-Tuning and Stochastic Normalization

3. Hidden Markov Model Based Respiratory Sound Classification

4. Medaug: Contrastive learning leveraging patient metadata improves representations for chest x-ray interpretation;vu,0

5. Contrastive learning of heart and lung sounds for label-efficient diagnosis;soni,2021

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1. GaP-Aug: Gamma Patch-Wise Correction Augmentation Method for Respiratory Sound Classification;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

2. Multi-View Spectrogram Transformer for Respiratory Sound Classification;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

3. Resilient embedded system for classification respiratory diseases in a real time;Biomedical Signal Processing and Control;2024-04

4. Attention Feature Fusion Network via Knowledge Propagation for Automated Respiratory Sound Classification;IEEE Open Journal of Engineering in Medicine and Biology;2024

5. Masked Modeling Duo: Towards a Universal Audio Pre-Training Framework;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024

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