This paper provides a review of gestural control of sound synthesis in the context of the design and evaluation of digital musical instruments. It discusses research in various areas related to this field and equally focuses on four main topics: analysis of music performers’ gestures, gestural capture technologies, real-time sound synthesis methods, and strategies for mapping gesture variables to sound synthesis input parameters. Finally, this approach is illustrated by presenting an application of this research to the control of digital audio effects.

Digital musical instruments do not depend on physical constraints faced by their acoustic counterparts, such as characteristics of tubes, membranes, strings, etc. This fact permits a huge diversity of possibilities regarding sound production, but on the other hand strategies to design and perform these new instruments need to be devised in order to provide the same level of control subtlety available in acoustic instruments. In this paper I review various topics related to gestural control of music using digital musical instruments and identify possible trends in this domain.

In this paper, we describe a multi-level approach for the extraction of instrumental gesture parameters taken from the characteristics of the signal captured by a microphone and basedonthe knowledge of physical mechanisms taking place on the instrument. We also explore the relationships between some features of timbre and gesture parameters, taking as a starting point for the exploration the timbre descriptors commonlyusedbyprofessionalmusicians when they verbally describe the sounds they produce with their instrument. Finally, we present how this multi-level approach can be applied to the study of the timbre space of the classical guitar.

Using sensors to extract gestural information for control parameters of digital audio effects is common practice. There has also been research using machine learning techniques to classify specific gestures based on audio feature analysis. In this paper, we will describe our experiments in training a computer to map the appropriate audio-based features to look like sensor data, in order to potentially eliminate the need for sensors. Specifically, we will show our experiments using the ESitar, a digitally enhanced sensor based controller modeled after the traditional North Indian sitar. We utilize multivariate linear regression to map continuous audio features to continuous gestural data. 1.