Toward Faithful Explanations in Acoustic Anomaly Detection

• URL: http://arxiv.org/abs/2601.12660v1
• Date: Mon, 19 Jan 2026 02:16:37 GMT
• Title: Toward Faithful Explanations in Acoustic Anomaly Detection
• Authors: Maab Elrashid, Anthony Deschênes, Cem Subakan, Mirco Ravanelli, Rémi Georges, Michael Morin,
• Abstract summary: Interpretability is essential for user trust in real-world anomaly detection applications.<n>We compare a standard autoencoder (AE) with a mask autoencoder (MAE) in terms of detection performance and interpretability.<n>MAE consistently provides more faithful and temporally precise explanations, suggesting a better alignment with true anomalies.
• Score: 21.487734134424187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interpretability is essential for user trust in real-world anomaly detection applications. However, deep learning models, despite their strong performance, often lack transparency. In this work, we study the interpretability of autoencoder-based models for audio anomaly detection, by comparing a standard autoencoder (AE) with a mask autoencoder (MAE) in terms of detection performance and interpretability. We applied several attribution methods, including error maps, saliency maps, SmoothGrad, Integrated Gradients, GradSHAP, and Grad-CAM. Although MAE shows a slightly lower detection, it consistently provides more faithful and temporally precise explanations, suggesting a better alignment with true anomalies. To assess the relevance of the regions highlighted by the explanation method, we propose a perturbation-based faithfulness metric that replaces them with their reconstructions to simulate normal input. Our findings, based on experiments in a real industrial scenario, highlight the importance of incorporating interpretability into anomaly detection pipelines and show that masked training improves explanation quality without compromising performance.

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