Lung-SRAD: Spectral-Aware Regularized Audio DASS with Dual-Axis Patch-Mix Contrastive Learning for Respiratory Sound Classification
Abstract
Recent respiratory sound classification (RSC) studies largely rely on CLS-token driven self-attention architectures such as the Audio Spectrogram Transformer (AST). While effective at modeling global context, recent analyses suggest a low-pass filtering behavior that may reduce sensitivity to localized abnormal patterns. In this work, we investigate State Space Models (SSMs) as an alternative backbone for RSC. Using the Distilled Audio State Space model, we analyze intermediate representations through spectral response curves and observe stronger preservation of mid-to-high spatial-frequency components. Based on these observations, we introduce spectral-aware layer regularization using Gaussian convolution applied to selected layers. We further propose Dual-Axis Patch-Mix contrastive learning tailored to SSM-based audio models for robust representation learning. Experiments on the ICBHI benchmark show that our approach achieves 64.48% score, outperforming the AST baseline by 5%. Code is available at https://github.com/RSC-Toolkit/Lung-SRAD.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper introduces a novel approach to respiratory sound classification using State Space Models, addressing key limitations of existing Transformer-based methods. The technical contributions, including spectral-aware regularization and contrastive learning, are well-founded and demonstrate potential for significant impact in the field of medical audio analysis.
Methodology
The paper presents a novel approach to respiratory sound classification (RSC) by leveraging State Space Models (SSMs) as an alternative to traditional Transformer architectures. The introduction of spectral-aware layer regularization and Dual-Axis Patch-Mix contrastive learning is well-motivated and addresses specific limitations of existing methods, particularly the low-pass filtering behavior of self-attention mechanisms. The methodology is clearly articulated, with a strong theoretical foundation and empirical validation of the proposed techniques.
Results & evaluation
The experiments are conducted on the ICBHI benchmark, which is a relevant dataset for RSC. The results demonstrate a clear improvement over the baseline Audio Spectrogram Transformer (AST) model, achieving a score of 64.48%. The paper provides a thorough analysis of the performance metrics, including sensitivity and specificity, which are crucial for medical applications. However, the paper could benefit from additional comparisons with more recent models in the field to further contextualize its contributions.
Reproducibility
The authors provide sufficient details regarding the experimental setup, including training parameters and evaluation metrics, which enhances reproducibility. The availability of the code on GitHub is a positive aspect, enabling other researchers to replicate the findings.
Limitations
One limitation of the study is the reliance on a single dataset (ICBHI), which may affect the generalizability of the results. Additionally, while the proposed methods show improvements, the paper does not explore the potential trade-offs in computational efficiency or model complexity compared to existing architectures.
Broader impact
The proposed method has significant implications for the field of respiratory sound classification, which is critical for diagnosing various respiratory conditions. By improving the sensitivity to abnormal lung sounds, this research could enhance clinical decision-making and patient outcomes in respiratory health. This paper introduces a novel approach to respiratory sound classification using State Space Models, addressing key limitations of existing Transformer-based methods. The technical contributions, including spectral-aware regularization and contrastive learning, are well-founded and demonstrate potential for significant impact in the field of medical audio analysis.
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