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Communication Dans Un Congrès Année : 2023

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

Résumé

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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Dates et versions

hal-04155178 , version 1 (07-07-2023)

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  • HAL Id : hal-04155178 , version 1

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Geoffroy Peeters. Self-Similarity-Based and Novelty-based loss for music structure analysis. Conference of the International Society for Music Information Retrieval, Nov 2023, Milano, Italy. ⟨hal-04155178⟩
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