Deep audio representation learning for music using weak supervision
Music audio tagging is the Music Information Retrieval task of assigning one or multiple labels to an audio signal. Current state-of-the-art music taggers rely on deep learning approaches, which offer high performance but introduce challenges due to their large data requirements and tendency to over...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/692389 |
| Acceso en línea: | http://hdl.handle.net/10803/692389 |
| Access Level: | acceso abierto |
| Palabra clave: | Representation learning Music Information Retrieval Music tagging Music classification Deep learning Audio processing Aprenentatge de representacions Recuperació d'Informació Musical Etiquetatge d'àudio musical Classificació musical Aprenentatge profund Processament d'àudio 62 |
| Sumario: | Music audio tagging is the Music Information Retrieval task of assigning one or multiple labels to an audio signal. Current state-of-the-art music taggers rely on deep learning approaches, which offer high performance but introduce challenges due to their large data requirements and tendency to overfit. In this thesis, we propose addressing music tagging from the perspective of representation learning, which consists of designing pre-training objectives that make the learned representations suitable for several downstream tasks. In our work we investigate using representations learned by competitive music and audio tagging systems, the effectiveness of training representation models on music metadata (such as artist names and playlists) as a source of supervision, the usage of the transformer architecture for representation learning, and the adaptation of audio interpretability strategies to operate with pre-trained representations. Most of the models developed in this thesis were incorporated into Essentia,1 an open-source sound and music analysis library. |
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