CoughLIME: Sonified Explanations for the Predictions of COVID-19 Cough Classifiers
Abstract
Since the emergence of the COVID-19 pandemic, various methods to detect the illness from cough and speech audio data have been proposed. While many of them deliver promising results, they lack transparency in the form of expla-nations which is crucial for establishing trust in the classifiers. We propose CoughLIME which extends LIME to explanations for audio data, specifically tailored towards cough data. We show that CoughLIME is capable of generating faithful sonified explanations for COVID-19 detection. To quantify the performance of the explanations generated for the CIdeR model, we adopt pixel flipping to audio and introduce a novel metric to assess the performance of the XAI classifier. CoughLIME achieves a ΔAUC of 19.48 % generating explanations for CIdeR's predictions.
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CoughLIME extended LIME to generate sonified explanations for COVID-19 cough analysis.
“CoughLIME (Cough Local Interpretable Model-Agnostic Explanations) [122] extended LIME to generate sonified explanations for COVID-19 cough analysis.”
From Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review (2026) · cites this paper as [122]
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- Authors
- Wullenweber, A., Akman, A., & Schuller, B. W.
- Affiliations
- Imperial College London (GB)
- Imperial College London,GLAM - Group on Language, Audio, & Music,London,UK
- ORCID
- Akman, Alican
- Venue
- 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2022-07-11
- Volume
- 2022
- Pages
- 1342-1345
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W4294975502
- Cited by
- 13
- References
- 19
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 3 · 2023: 2
- FWCI
- 1.52
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Keywords
- lime, explainable AI, COVID-19 detection, cough classification, audio data, explanation evaluation metrics
- MeSH terms
- COVID-19, Cough, Humans, Speech, Pandemics
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