RUMAA: Repeat-Aware Unified Music Audio Analysis for Score-Performance Alignment, Transcription, and Mistake Detection

• URL: http://arxiv.org/abs/2507.12175v1
• Date: Wed, 16 Jul 2025 12:13:13 GMT
• Title: RUMAA: Repeat-Aware Unified Music Audio Analysis for Score-Performance Alignment, Transcription, and Mistake Detection
• Authors: Sungkyun Chang, Simon Dixon, Emmanouil Benetos,
• Abstract summary: RUMAA is a transformer-based framework for music performance analysis.<n>It unifies score-to-performance alignment, score-informed transcription, and mistake detection in a near end-to-end manner.
• Score: 17.45655063331199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study introduces RUMAA, a transformer-based framework for music performance analysis that unifies score-to-performance alignment, score-informed transcription, and mistake detection in a near end-to-end manner. Unlike prior methods addressing these tasks separately, RUMAA integrates them using pre-trained score and audio encoders and a novel tri-stream decoder capturing task interdependencies through proxy tasks. It aligns human-readable MusicXML scores with repeat symbols to full-length performance audio, overcoming traditional MIDI-based methods that rely on manually unfolded score-MIDI data with pre-specified repeat structures. RUMAA matches state-of-the-art alignment methods on non-repeated scores and outperforms them on scores with repeats in a public piano music dataset, while also delivering promising transcription and mistake detection results.

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