CoughLIME: Sonified Explanations for the Predictions of COVID-19 Cough Classifiers

Anne Wullenweber, Alican Akman, Bjorn W. Schuller

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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.

Original languageEnglish
Title of host publication44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1342-1345
Number of pages4
ISBN (Electronic)9781728127828
DOIs
StatePublished - 2022
Externally publishedYes
Event44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 - Glasgow, United Kingdom
Duration: 11 Jul 202215 Jul 2022

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2022-July
ISSN (Print)1557-170X

Conference

Conference44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Country/TerritoryUnited Kingdom
CityGlasgow
Period11/07/2215/07/22

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