Automatic characterization and generation of music loops and instrument samples for electronic music production

Ramires, António

Automatic characterization and generation of music loops and instrument samples for electronic music production

Tipus de document

Tesi doctoral
Versió publicada

Autoria

Ramires, António

Director/a

Serra, Xavier
Font Corbera, Frederic

Data de publicació

2023-02-08

Pàgines

182 p.

Llicència

Citació

Ramires, A. Automatic characterization and generation of music loops and instrument samples for electronic music production. Universitat Pompeu Fabra; 2023. handle: http://hdl.handle.net/10803/687697

Aquesta citació s'ha generat automàticament.

Programa de doctorat

Programa de doctorat en Tecnologies de la Informació i les Comunicacions

Resum

Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process. We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation.
Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process. We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation.

Paraules Clau

Electronic music production, Instrument classification, Percussive sound generation, Music information retrieval, Deep learning, Deep generative models, Producción de música electrónica, Clasificación de instrumentos, Generación de sonidos percusivos, Recuperación de la información musical, Aprendizaje profundo, Modelos generativos profundos