Analysis of ensemble expressive performance in string quartets: a statistical and machine learning approach
Computational approaches for modeling expressive music performance have produced systems that emulate human expression, but few steps have been taken in the domain of ensemble performance. Polyphonic expression and inter-dependence among voices are intrinsic features of ensemble performance and need...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/285204 |
| Acceso en línea: | http://hdl.handle.net/10803/285204 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine-learning Music performance Ensemble performance Expressive performance String quartets Aprenentatge automàtic Expressivitat musical Conjunts musicals Quartets de corda Aprendizaje automático Expresividad musical Conjuntos musicales Cuartetos de cuerda 62 |
| Sumario: | Computational approaches for modeling expressive music performance have produced systems that emulate human expression, but few steps have been taken in the domain of ensemble performance. Polyphonic expression and inter-dependence among voices are intrinsic features of ensemble performance and need to be incorporated at the very core of the models. For this reason, we proposed a novel methodology for building computational models of ensemble expressive performance by introducing inter-voice contextual attributes (extracted from ensemble scores) and building separate models of each individual performer in the ensemble. We focused our study on string quartets and recorded a corpus of performances both in ensemble and solo conditions employing multi-track recording and bowing motion acquisition techniques. From the acquired data we extracted bowed-instrument-specific expression parameters performed by each musician. As a preliminary step, we investigated over the difference between solo and ensemble from a statistical point of view and show that the introduced inter-voice contextual attributes and extracted expression are statistically sound. In a further step, we build models of expression by training machine-learning algorithms on the collected data. As a result, the introduced inter-voice contextual attributes improved the prediction of the expression parameters. Furthermore, results on attribute selection show that the models trained on ensemble recordings took more advantage of inter-voice contextual attributes than those trained on solo recordings. The obtained results show that the introduced methodology can have applications in the analysis of collaboration among musicians. |
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