Exploring single-song autoencoding schemes for audio-based music structure analysis

• URL: http://arxiv.org/abs/2110.14437v1
• Date: Wed, 27 Oct 2021 13:48:25 GMT
• Title: Exploring single-song autoencoding schemes for audio-based music structure analysis
• Authors: Axel Marmoret, J\'er\'emy E. Cohen, Fr\'ed\'eric Bimbot
• Abstract summary: This work explores a "piece-specific" autoencoding scheme, in which a low-dimensional autoencoder is trained to learn a latent/compressed representation specific to a given song. We report that the proposed unsupervised auto-encoding scheme achieves the level of performance of supervised state-of-the-art methods with 3 seconds tolerance.
• Score: 6.037383467521294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability of deep neural networks to learn complex data relations and representations is established nowadays, but it generally relies on large sets of training data. This work explores a "piece-specific" autoencoding scheme, in which a low-dimensional autoencoder is trained to learn a latent/compressed representation specific to a given song, which can then be used to infer the song structure. Such a model does not rely on supervision nor annotations, which are well-known to be tedious to collect and often ambiguous in Music Structure Analysis. We report that the proposed unsupervised auto-encoding scheme achieves the level of performance of supervised state-of-the-art methods with 3 seconds tolerance when using a Log Mel spectrogram representation on the RWC-Pop dataset.

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