New AI Model Improves Respiratory Sound Classification Using State Space Models
Researchers have proposed Lung-SRAD, a new deep learning framework for classifying respiratory sounds that outperforms the standard Audio Spectrogram Transformer baseline by 5% on a key benchmark. The system uses State Space Models instead of conventional transformer architectures, combined with spectral-aware regularization and a novel contrastive learning technique. The work could advance automated screening tools for respiratory diseases such as asthma and COPD.
A team of researchers has introduced Lung-SRAD, a machine learning system designed to classify abnormal respiratory sounds from audio recordings, achieving a score of 64.48% on the widely used ICBHI benchmark. The approach departs from dominant transformer-based methods, specifically the Audio Spectrogram Transformer (CLS-token architecture), which the authors argue exhibits low-pass filtering behavior that may suppress sensitivity to localized abnormal acoustic patterns. Instead, the system is built on State Space Models (SSMs), which the researchers found to better preserve mid-to-high spatial-frequency components in intermediate audio representations. Two key innovations are introduced: spectral-aware layer regularization using Gaussian convolution, and Dual-Axis Patch-Mix contrastive learning tailored for SSM-based audio models. The paper was accepted to Interspeech 2026, and code has been made publicly available. While the 5% improvement over the AST baseline is notable, the absolute score of 64.48% indicates that respiratory sound classification remains a challenging open problem.
What's missing
External validation on datasets beyond ICBHI is not reported, leaving generalizability to real-world clinical audio uncertain. The computational cost and inference speed of the SSM-based approach relative to the AST baseline are not discussed.
What different sources said
- arXiv cs.AICenter
Lung-SRAD: Spectral-Aware Regularized Audio DASS with Dual-Axis Patch-Mix Contrastive Learning for Respiratory Sound Classification
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