2026
Toward Faithful Explanations in Acoustic Anomaly Detection
ICASSP 2026poster
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 sta…