Explainability of CNN Based Classification Models for Acoustic Signal
Abstract
Explainable Artificial Intelligence (XAI) has emerged as a critical tool for interpreting the predictions of complex deep learning models. While XAI has been increasingly applied in various domains within acoustics, its use in bioacoustics, which involves analyzing audio signals from living organisms, remains relatively underexplored. In this paper, we investigate the vocalizations of a bird species with strong geographic variation throughout its range in North America. Audio recordings were converted into spectrogram images and used to train a deep Convolutional Neural Network (CNN) for classification, achieving an accuracy of 94.8%. To interpret the model's predictions, we applied both model-agnostic (LIME, SHAP) and model-specific (DeepLIFT, Grad-CAM) XAI techniques. These techniques produced different but complementary explanations, and when their explanations were considered together, they provided more complete and interpretable insights into the model's decision-making. This work highlights the importance of using a combination of XAI techniques to improve trust and interpretability, not only in broader acoustics signal analysis but also argues for broader applicability in different domain-specific tasks.
Department(s)
Computer Science
Document Type
Conference Proceeding
DOI
10.1109/ICTAI66417.2025.00159
Keywords
Bioacoustics, DeepLIFT, Grad-CAM, LIME, SHAP, XAI
Publication Date
1-1-2025
Recommended Citation
Faruqui, Zubair; McIntire, Mackenzie S.; Dubey, Rahul; and McEntee, Jay, "Explainability of CNN Based Classification Models for Acoustic Signal" (2025). Faculty Scholarship. 231.
https://bearworks.missouristate.edu/articles00/231
Journal Title
Proceedings International Conference on Tools with Artificial Intelligence Ictai
