A Hybrid Approach to Audio-to-Score Alignment
Audio and Speech Processing
2020-07-29 v1 Machine Learning
Sound
Abstract
Audio-to-score alignment aims at generating an accurate mapping between a performance audio and the score of a given piece. Standard alignment methods are based on Dynamic Time Warping (DTW) and employ handcrafted features. We explore the usage of neural networks as a preprocessing step for DTW-based automatic alignment methods. Experiments on music data from different acoustic conditions demonstrate that this method generates robust alignments whilst being adaptable at the same time.
Keywords
Cite
@article{arxiv.2007.14333,
title = {A Hybrid Approach to Audio-to-Score Alignment},
author = {Ruchit Agrawal and Simon Dixon},
journal= {arXiv preprint arXiv:2007.14333},
year = {2020}
}
Comments
ML4MD at ICML 2019