Data-driven musical version identification: accuracy, scalability and bias perspectives
This dissertation aims at developing audio-based musical version identification (VI) systems for industry-scale corpora. To employ such systems in industrial use cases, they must demonstrate high performance on large-scale corpora while not favoring certain musicians or tracks above others. Therefor...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/673264 |
| Acceso en línea: | http://hdl.handle.net/10803/673264 |
| Access Level: | acceso abierto |
| Palabra clave: | Music information retrieval Music similarity Music processing Audio processing Deep learning Representation learning Music embeddings Metric learning Embedding distillation Algorithmic bias Cover songs Recuperación de información musical Similitud musical Procesamiento de música Procesamiento de audio Aprendizaje profundo Aprendizaje de representación Aprendizaje métrico Sesgo algorítmico 62 |
| Sumario: | This dissertation aims at developing audio-based musical version identification (VI) systems for industry-scale corpora. To employ such systems in industrial use cases, they must demonstrate high performance on large-scale corpora while not favoring certain musicians or tracks above others. Therefore, the three main aspects we address in this dissertation are accuracy, scalability, and algorithmic bias of VI systems. We propose a data-driven model that incorporates domain knowledge in its network architecture and training strategy. We then take two main directions to further improve our model. Firstly, we experiment with data-driven fusion methods to combine information from models that process harmonic and melodic information, which greatly enhances identification accuracy. Secondly, we investigate embedding distillation techniques to reduce the size of the embeddings produced by our model, which reduces the requirements for data storage and, more importantly, retrieval time. Lastly, we analyze the algorithmic biases of our systems. |
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