Work

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

Year: 2022

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2211.08141

Authors: Peeters, Geoffroy, Angulo, Florian

Institutions: Télécom Paris, Institut Polytechnique de Paris, Laboratoire Traitement et Communication de l’Information

Language: English

Topic: Music and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4309209178

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