Improving Deep Learning-based Respiratory Sound Analysis with Frequency Selection and Attention Mechanism

• URL: http://arxiv.org/abs/2507.20052v1
• Date: Sat, 26 Jul 2025 20:29:25 GMT
• Title: Improving Deep Learning-based Respiratory Sound Analysis with Frequency Selection and Attention Mechanism
• Authors: Nouhaila Fraihi, Ouassim Karrakchou, Mounir Ghogho,
• Abstract summary: We propose a compact CNN-Temporal Self-Attention (CNN-TSA) network that integrates lightweight self-attention into an efficient CNN backbone.<n>Central to our approach is a Frequency Band Selection (FBS) module that suppresses noisy and non-informative frequency regions.<n>We also introduce age-specific models to enhance robustness across diverse patient groups.
• Score: 3.1515385358176817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate classification of respiratory sounds requires deep learning models that effectively capture fine-grained acoustic features and long-range temporal dependencies. Convolutional Neural Networks (CNNs) are well-suited for extracting local time-frequency patterns but are limited in modeling global context. In contrast, transformer-based models can capture long-range dependencies, albeit with higher computational demands. To address these limitations, we propose a compact CNN-Temporal Self-Attention (CNN-TSA) network that integrates lightweight self-attention into an efficient CNN backbone. Central to our approach is a Frequency Band Selection (FBS) module that suppresses noisy and non-informative frequency regions, substantially improving accuracy and reducing FLOPs by up to 50%. We also introduce age-specific models to enhance robustness across diverse patient groups. Evaluated on the SPRSound-2022/2023 and ICBHI-2017 lung sound datasets, CNN-TSA with FBS sets new benchmarks on SPRSound and achieves state-of-the-art performance on ICBHI, all with a significantly smaller computational footprint. Furthermore, integrating FBS into an existing transformer baseline yields a new record on ICBHI, confirming FBS as an effective drop-in enhancement. These results demonstrate that our framework enables reliable, real-time respiratory sound analysis suitable for deployment in resource-constrained settings.

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