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

• URL: http://arxiv.org/abs/2211.08141v1
• Date: Tue, 15 Nov 2022 13:48:11 GMT
• Title: SSM-Net: feature learning for Music Structure Analysis using a Self-Similarity-Matrix based loss
• Authors: Geoffroy Peeters and Florian Angulo
• Abstract summary: We train a deep encoder to learn features such that the Self-Similarity-Matrix (SSM) resulting from those approximates a ground-truth SSM. We successfully demonstrate the use of this training paradigm using the Area Under the Curve ROC (AUC) on the RWC-Pop dataset.
• Score: 7.599399338954308
• License: http://creativecommons.org/licenses/by/4.0/
• 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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