Audio-to-Score Alignment using Transposition-invariant Features
Abstract
Audio-to-score alignment is an important pre-processing step for in-depth analysis of classical music. In this paper, we apply novel transposition-invariant audio features to this task. These low-dimensional features represent local pitch intervals and are learned in an unsupervised fashion by a gated autoencoder. Our results show that the proposed features are indeed fully transposition-invariant and enable accurate alignments between transposed scores and performances. Furthermore, they can even outperform widely used features for audio-to-score alignment on `untransposed data', and thus are a viable and more flexible alternative to well-established features for music alignment and matching.
Cite
@article{arxiv.1807.07278,
title = {Audio-to-Score Alignment using Transposition-invariant Features},
author = {Andreas Arzt and Stefan Lattner},
journal= {arXiv preprint arXiv:1807.07278},
year = {2018}
}
Comments
19th International Society for Music Information Retrieval Conference, Paris, France, 2018