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arXiv · 2211.08141

SSM-Net: feature learning for Music Structure Analysis using a Self-Similarity-Matrix based loss

Geoffroy Peeters·Florian Angulo
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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.

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BibTeXRIS

Geoffroy Peeters, Florian Angulo. 2022-11-15. SSM-Net: feature learning for Music Structure Analysis using a Self-Similarity-Matrix based loss. https://arxiv.org/abs/2211.08141

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