SchenkerLink: Human-in-the-Loop Hierarchical Music Analysis with Uncertainty-Aware Graph Link Prediction

Abstract

SchenkerLink is a human-in-the-loop framework for hierarchical music analysis that leverages uncertainty-aware graph link prediction to model voice-leading and prolongational structures in symbolic music. Unlike prior systems that rely on fixed heuristics or fully automated approaches, SchenkerLink combines the strengths of machine learning and expert feedback to improve interpretability and robustness in music-theoretic analysis. By incorporating uncertainty quantification into the link-prediction process, the system guides analysts toward ambiguous or structurally significant passages, making the analytical process more efficient and musically meaningful. Results demonstrate that SchenkerLink not only improves predictive performance but also supports deeper collaboration between computational models and human music theorists.