SSM-Net: feature learning for Music Structure Analysis using a Self-Similarity-Matrix based loss
Sound
2022-11-16 v1 Machine Learning
Audio and Speech Processing
Abstract
In this paper, we propose a new paradigm to learn audio features for Music Structure Analysis (MSA). We train a deep encoder to learn features such that the Self-Similarity-Matrix (SSM) resulting from those approximates a ground-truth SSM. This is done by minimizing a loss between both SSMs. Since this loss is differentiable w.r.t. its input features we can train the encoder in a straightforward way. We successfully demonstrate the use of this training paradigm using the Area Under the Curve ROC (AUC) on the RWC-Pop dataset.
Cite
@article{arxiv.2211.08141,
title = {SSM-Net: feature learning for Music Structure Analysis using a Self-Similarity-Matrix based loss},
author = {Geoffroy Peeters and Florian Angulo},
journal= {arXiv preprint arXiv:2211.08141},
year = {2022}
}
Comments
Extended Abstracts for the Late-Breaking Demo Session of the 23rd Int. Society for Music Information Retrieval Conf., Bengaluru, India, 2022