We present a machine learning approach to the problem of identifying performers from their interpretative styles. In particular, we investigate how violinists express their view of the musical content in audio recordings ...

The authors provide a first-person outlook on the technical challenges involved in the recording, analysis, archiving, and cloud-based interchange of multimodal string quartet performance data as part of a collaborative ...

We present a method for the analysis of the finger-string interaction in guitar performances and the computation of fine actions during the plucking gesture. The method is based on Motion Capture using high-speed cameras ...

In a musical ensemble such as a string quartet, the musicians interact and influence each other's actions in several aspects of the performance simultaneously in order to achieve a common aesthetic goal. In this article, ...

In this article we present our ongoing work on expressive /nperformance analysis for violin and string ensembles, in terms of synchronization in intonation, timing, dynamics and articulation. Our current /nresearch objectives ...

Background: Expression is a key skill in music performance, and one that is difficult to address in music lessons. Computational models that learn from expert performances can help providing suggestions and feedback to ...

Excitation-continuous music instrument control patterns are often not explicitly represented in current sound synthesis techniques when applied to automatic performance. Both physical model-based and sample-based synthesis ...