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AsthmaSCELNet: A Lightweight Supervised Contrastive Embedding Learning Framework For Asthma Classification Using Lung Sounds

1 Aug 2023Interspeech 2023 8archive 2025-07-28

Arka Roy, Udit Satija

Asthma is one of the most prevalent respiratory disorders, which can be identified by different modalities such as speech, wheezing of lung sounds (LSs), spirometric measures, etc. In this paper, we propose AsthmaSCELNet, a lightweight supervised contrastive embedding learning framework, to classify asthmatic LSs by providing adequate classification margin across the embeddings of healthy and asthma LS, in contrast to vanilla supervised learning. Our proposed framework consists of three steps: pre-processing, melspectrogram extraction, and classification. The AsthmaSCELNet consists of two stages: embedding learning using a lightweight embedding extraction backbone module that extracts compact embedding from the melspectrogram, and classification by the learnt embeddings using multi-layer perceptrons. The proposed framework achieves an accuracy, sensitivity, and specificity of 98.54%, 98.27%, and 98.73% respectively, that outperforms existing methods based on LSs and other modalities.

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rsarka34/AsthmaSCELNet-INTERSPEECH tf report

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Tasks

Asthmatic Lung Sound Classification Audio Classification Classification Diagnostic Lung Disease Classification Lung Sound Classification Specificity

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Asthmatic Lung Sound Classification	Chest wall lung sound dataset	AsthmaSCELNet	2-Class Accuracy	98.54	#1 of 2	Archive leaderboard	report

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