27 Nov 2014

Marco Marchini defends his PhD thesis entitled "Analysis of Ensemble Expressive Performance in String Quartets: a Statistical and Machine Learning Approach" on Thursday November 27th 2014 at 12:00h in room 55.309 of the Communication Campus of the UPF.

The jury of the defense is: Xavier Serra (UPF), Josep Lluís Arcos (IIIA-CSIC), Roberto Bresin (KTH).

Abstract: 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-instrumentspecic expression parameters performed by each musician. As a preliminary step, we investigated over the dierence 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.