Exploring single-song autoencoding schemes for audio-based music structure analysis
| Autor: | Marmoret, Axel, Cohen, J��r��my E., Bimbot, Fr��d��ric |
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| Rok vydání: | 2021 |
| Předmět: | |
| DOI: | 10.48550/arxiv.2110.14437 |
| Popis: | 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. 4 pages, 4 figures, 2 tables. Rejected from ICASSP 2022, an extended version is available at arXiv:2202.04981 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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