APEX: Audio Prototype EXplanations for Classification Tasks

• URL: http://arxiv.org/abs/2605.10153v1
• Date: Mon, 11 May 2026 08:00:38 GMT
• Title: APEX: Audio Prototype EXplanations for Classification Tasks
• Abstract summary: APEX (Audio Prototype EXplanations) is a framework for interpreting pre-trained audio classifiers.<n>It disentangles explanations into four perspectives: Square-based prototypes to localize transient events, Time-based for temporal patterns, Frequency-based highlighting spectral bands, and Time-Frequency-based integrating both.<n>It provides greater semantic clarity than standard gradient-based methods.
• Score: 4.385522611751555
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Explainable AI (XAI) has achieved remarkable success in image classification, yet the audio domain lacks equally mature solutions. Current methods apply vision-based attribution techniques to spectrograms, overlooking fundamental differences between visual and acoustic signals. While prototype reasoning is promising, acoustic similarity remains multidimensional. We introduce APEX (Audio Prototype EXplanations), a post-hoc framework for interpreting pre-trained audio classifiers. Crucially, APEX requires no fine-tuning of the original backbone and strictly preserves output invariance. APEX disentangles explanations into four perspectives: Square-based prototypes to localize transient events, Time-based for temporal patterns, Frequency-based highlighting spectral bands, and Time-Frequency-based integrating both. This yields intuitive, example-based explanations that respect acoustic properties, providing greater semantic clarity than standard gradient-based methods.

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