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arXiv · 2509.26521

MUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Models

Baptiste Hilaire·Emmanouil Karystinaios·Gerhard Widmer
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Abstract

Interpretability is essential for deploying deep learning models in symbolic music analysis, yet most research emphasizes model performance over explanation. To address this, we introduce MUSE-Explainer, a new method that helps reveal how music Graph Neural Network models make decisions by providing clear, human-friendly explanations. Our approach generates counterfactual explanations by making small, meaningful changes to musical score graphs that alter a model's prediction while ensuring the results remain musically coherent. Unlike existing methods, MUSE-Explainer tailors its explanations to the structure of musical data and avoids unrealistic or confusing outputs. We evaluate our method on a music analysis task and show it offers intuitive insights that can be visualized with standard music tools such as Verovio.

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Baptiste Hilaire, Emmanouil Karystinaios, Gerhard Widmer. 2025-09-30. MUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Models. https://arxiv.org/abs/2509.26521

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