We report preliminary results of an ongoing project on automatic recognition and classification of musical “gestures” from audio extracts. We use a machine learning tool designed for motion tracking and recognition, applied to labeled vectors of audio descriptors in order to recognize hypothetical gestures formed by these descriptors. A hypothesis is that the classes detected in audio descriptors can be used to identify higher-level/abstract musical structures which might not be described easily using standard/symbolic representations.

We present a new framework for reactive programming in OpenMusic, a visual programming language dedicated to computer-aided music composition. We highlight some characteristics of the programming and computation paradigms, and describe the implementation of a hybrid system merging demand-driven and event-driven evaluation models in this environment.

OpenMusic is an open source environment dedicated to music composition. The core of this environment is a full-featured visual programming language based on Common Lisp and CLOS (Common Lisp Object System) allowing to design processes for the generation or manipulation of musical material. This language can also be used for general purpose visual programming and other (possibly extra-musical) applications.

We present score programming features in the visual programming and computer-aided composition environment OpenMusic. The sheet object allows to build complex scores and fill or modify their contents algorithmically using visual programs.