Self-Similarity-Based and Novelty-based loss for music structure analysis

• URL: http://arxiv.org/abs/2309.02243v1
• Date: Tue, 5 Sep 2023 13:49:29 GMT
• Title: Self-Similarity-Based and Novelty-based loss for music structure analysis
• Authors: Geoffroy Peeters
• Abstract summary: We propose a supervised approach for the task of music boundary detection. In our approach we simultaneously learn features and convolution kernels. We demonstrate that relative feature learning, through self-attention, is beneficial for the task of MSA.
• Score: 5.3900692419866285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Music Structure Analysis (MSA) is the task aiming at identifying musical segments that compose a music track and possibly label them based on their similarity. In this paper we propose a supervised approach for the task of music boundary detection. In our approach we simultaneously learn features and convolution kernels. For this we jointly optimize -- a loss based on the Self-Similarity-Matrix (SSM) obtained with the learned features, denoted by SSM-loss, and -- a loss based on the novelty score obtained applying the learned kernels to the estimated SSM, denoted by novelty-loss. We also demonstrate that relative feature learning, through self-attention, is beneficial for the task of MSA. Finally, we compare the performances of our approach to previously proposed approaches on the standard RWC-Pop, and various subsets of SALAMI.

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