Audio-to-Score Alignment Using Deep Automatic Music Transcription

2021/07/27 by Federico Simonetta, Simonetta, Federico, Stavros Ntalampiras +3 · 2 citations
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Multimedia (cs.MM) #Music Technology and Sound Studies #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2107.12854

openalex publication_date 2021/07/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Audio-to-score alignment (A2SA) is a multimodal task consisting in the alignment of audio signals to music scores. Recent literature confirms the benefits of Automatic Music Transcription (AMT) for A2SA at the frame-level. In this work, we aim to elaborate on the exploitation of AMT Deep Learning (DL) models for achieving alignment at the note-level. We propose a method which benefits from HMM-based score-to-score alignment and AMT, showing a remarkable advancement beyond the state-of-the-art. We design a systematic procedure to take advantage of large datasets which do not offer an aligned score. Finally, we perform a thorough comparison and extensive tests on multiple datasets.

Cited by

• RUMAA: Repeat-Aware Unified Music Audio Analysis for Score-Performance Alignment, Transcription, and Mistake Detection
• Fine-Tuning MIDI-to-Audio Alignment using a Neural Network on Piano Roll and CQT Representations

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