Transformer-based real-time automatic error annotation for piano performance

This research tackles the pressing challenge of real-time automatic error detection in piano performance, a task where conventional approaches often propagate inaccuracies due to the decoupling of audio-score alignment and error identification.This paper introduce the DiffAlign-Transformer framework, which incorporates a differentiable dynamic programming mechanism to jointly learn probabilistic note-level alignment and error classification within a hierarchical cross-modal encoder.Evaluated on the Vienna Synchronous Library dataset using a leave-one-performer-out validation strategy, the model attains an overall F1-score of 0.872, exceeding the strongest baseline by 6.0%, with marked gains in onset (7.2%) and offset (8.1%) error recognition.Inference requires only 78 milliseconds per second of audio, satisfying strict real-time constraints.These outcomes confirm that our method successfully resolves the intertwined alignment-detection problem and delivers precise, instantaneous feedback for piano pedagogy.