Audio-to-Score Alignment Using Deep Automatic Music Transcription
Sound
2022-01-03 v3 Multimedia
Audio and Speech Processing
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.
Cite
@article{arxiv.2107.12854,
title = {Audio-to-Score Alignment Using Deep Automatic Music Transcription},
author = {Federico Simonetta and Stavros Ntalampiras and Federico Avanzini},
journal= {arXiv preprint arXiv:2107.12854},
year = {2022}
}
Comments
IEEE MMSP 2021 - ERRATUM