Analyzing Music Improvisations Using Unsupervised Machine Learning: Towards Automatically Discovering Creative Cognition Principles
In the field of musical expression, the complex relationship between improvisation and the cognitive processes that underlie creativity presents a fascinating yet challenging puzzle, prompting this thesis to explore the connection between musical improvisation and creative cognition among musicians....
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/410520 |
| Acceso en línea: | https://hdl.handle.net/2117/410520 |
| Access Level: | acceso abierto |
| Palabra clave: | Improvisation (Music) Machine learning Music Improvisation Unsupervised Machine Learning Creative Cognition Improvisació (Música) Aprenentatge automàtic Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | In the field of musical expression, the complex relationship between improvisation and the cognitive processes that underlie creativity presents a fascinating yet challenging puzzle, prompting this thesis to explore the connection between musical improvisation and creative cognition among musicians. Focusing on the development of robust methods for feature extraction and representation, it utilizes unsupervised Machine Learning (ML) techniques to project improvisations from a prime melody into a high-level latent space. The methodology involves iterative analysis employing Variational Autoencoder (VAE) models, initially pre-trained with a larger dataset and fine-tuned with a musical improvisation dataset provided by the Max Plank Institute. Evaluation encompasses Evidence Lower Bound (ELBO) loss metric and dimensionality reduction techniques like Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Multidimensional Scaling (MDS), and Uniform Manifold Approximation and Projection (UMAP) to explore latent space representations. The results reveal that experienced musicians exhibit a greater divergence from the prime melody compared to amateurs. Moreover, professionals' samples demonstrate more refined clustering and nuanced adjustments between improvisations projected in the latent space. |
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