Cough Classification using Few-Shot Learning

• URL: http://arxiv.org/abs/2509.09515v1
• Date: Thu, 11 Sep 2025 14:56:47 GMT
• Title: Cough Classification using Few-Shot Learning
• Authors: Yoga Disha Sendhil Kumar, Manas V Shetty, Sudip Vhaduri,
• Abstract summary: We leverage Prototypical Networks with spectrogram representations of cough sounds to address the challenge of limited labeled data.<n>Our study evaluates whether few-shot learning can enable models to achieve performance comparable to traditional deep learning approaches.<n> Experimental findings show that few-shot learning models can achieve competitive accuracy.
• Score: 0.7136933021609079
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates the effectiveness of few-shot learning for respiratory sound classification, focusing on coughbased detection of COVID-19, Flu, and healthy conditions. We leverage Prototypical Networks with spectrogram representations of cough sounds to address the challenge of limited labeled data. Our study evaluates whether few-shot learning can enable models to achieve performance comparable to traditional deep learning approaches while using significantly fewer training samples. Additionally, we compare multi-class and binary classification models to assess whether multi-class models can perform comparably to their binary counterparts. Experimental findings show that few-shot learning models can achieve competitive accuracy. Our model attains 74.87% accuracy in multi-class classification with only 15 support examples per class, while binary classification achieves over 70% accuracy across all class pairs. Class-wise analysis reveals Flu as the most distinguishable class, and Healthy as the most challenging. Statistical tests (paired t-test p = 0.149, Wilcoxon p = 0.125) indicate no significant performance difference between binary and multiclass models, supporting the viability of multi-class classification in this setting. These results highlight the feasibility of applying few-shot learning in medical diagnostics, particularly when large labeled datasets are unavailable.

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