XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917277288366080 |
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| author | Amado-Caballero, Patricia San-José-Revuelta, Luis Miguel Aguilar-García, María Dolores Garmendia-Leiza, José Ramón Alberola-López, Carlos Casaseca-de-la-Higuera, Pablo |
| author_facet | Amado-Caballero, Patricia San-José-Revuelta, Luis Miguel Aguilar-García, María Dolores Garmendia-Leiza, José Ramón Alberola-López, Carlos Casaseca-de-la-Higuera, Pablo |
| contents | This paper proposes an eXplainable Artificial Intelligence (XAI)-driven methodology to enhance the understanding of cough sound analysis for respiratory disease management. We employ occlusion maps to highlight relevant spectral regions in cough spectrograms processed by a Convolutional Neural Network (CNN). Subsequently, spectral analysis of spectrograms weighted by these occlusion maps reveals significant differences between disease groups, particularly in patients with COPD, where cough patterns appear more variable in the identified spectral regions of interest. This contrasts with the lack of significant differences observed when analyzing raw spectrograms. The proposed approach extracts and analyzes several spectral features, demonstrating the potential of XAI techniques to uncover disease-specific acoustic signatures and improve the diagnostic capabilities of cough sound analysis by providing more interpretable results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14949 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization Amado-Caballero, Patricia San-José-Revuelta, Luis Miguel Aguilar-García, María Dolores Garmendia-Leiza, José Ramón Alberola-López, Carlos Casaseca-de-la-Higuera, Pablo Sound Machine Learning Audio and Speech Processing Signal Processing This paper proposes an eXplainable Artificial Intelligence (XAI)-driven methodology to enhance the understanding of cough sound analysis for respiratory disease management. We employ occlusion maps to highlight relevant spectral regions in cough spectrograms processed by a Convolutional Neural Network (CNN). Subsequently, spectral analysis of spectrograms weighted by these occlusion maps reveals significant differences between disease groups, particularly in patients with COPD, where cough patterns appear more variable in the identified spectral regions of interest. This contrasts with the lack of significant differences observed when analyzing raw spectrograms. The proposed approach extracts and analyzes several spectral features, demonstrating the potential of XAI techniques to uncover disease-specific acoustic signatures and improve the diagnostic capabilities of cough sound analysis by providing more interpretable results. |
| title | XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization |
| topic | Sound Machine Learning Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2508.14949 |