MIRages: an account of music audio extractors, semantic description and context-awareness, in the three ages of MIR
This tesis reports on research carried out and published during the last twenty years on different problems of Music Information Retrieval (MIR). We organize the text as a personal account and critical reflection along four hypothesized ages that have shaped the evolution of MIR. In the age of featu...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/666953 |
| Acceso en línea: | http://hdl.handle.net/10803/666953 |
| Access Level: | acceso abierto |
| Palabra clave: | Music information retrieval Audio analysis Music analysis Automatic classification of music Semantic features Audio features Timbre Music creation systems Music recommendation Cerca d’informació musical Anàlisi del so Classificació automàtica de música Descriptors semàntics Descriptors del so Sistemes de creació musical Recomanació musical 62 |
| Sumario: | This tesis reports on research carried out and published during the last twenty years on different problems of Music Information Retrieval (MIR). We organize the text as a personal account and critical reflection along four hypothesized ages that have shaped the evolution of MIR. In the age of feature extractors, we present work on features to describe sounds and music, especially timbre and tonal aspects. In the age of semantic descriptors work on describing music with high-level concepts, such as mood, instruments, similarities, cover versions or genres, usually inferred with machine learning from annotated collections is reported. In the age of context-aware systems we report on user models for recommendation and for avatar generation, in addition to factors that influence music listening decisions. We finally discuss the possibility of a more recent age of creative systems where MIR features, classifiers, models and evaluation methodologies aid to enhance or expand music creation. |
|---|