Ce travail s'inscrit dans la recherche en acquisition des connaissances et en apprentissage automatique pour la modélisation et la validation incrémentale de connaissances de résolution de problème. Nous proposons un modèle simple de représentation des connaissances opératoires qui s'appuie sur la notion de situation, et présentons un outil de modé...
In this paper we present jMax, the new editing and control system for Ircam's real-time musical applications. More than a simple evolution of MAX, jMax is a completely new and extensible environment. The components of the architecture will be presented, as well as its application framework.
In this paper we demonstrate the use of jMax, the new editing andcontrol system for Ircam's real-time musical applications. We detailits architecture, and present in application the different componentsof the system, including the scripting and control panel buildingfacilities. Finally, we demonstrate some musical applications.
In this paper we present the latest evolution of the execution engine of jMax, the Ircam environment for real-time musical applications. Already known as FTS, thisDSP engine has now evolved into the virtual machine of a language which is a superset of the well-known Max "message system." Details of the new architecture willbe presented, as well as ...
This article presents ESCHER, a sound synthesis environment based on Ircam's real-time audio environment jMax. ESCHER is a modular system providing synthesis-independent prototyping of gesturally-controlled instruments by means of parameter interpolation. The system divides into two components: gestural controller and synthesis engine. Mapping betw...
jMax est une nouvelle implémentation du langage visuel MAX, largement utilisé pour des applications musicales interactives. Basé sur une architecture mixte JAVA/C, jMax privilégie la portabilité.
Nowadays, data mining is an active domain that is linked to data management and machine learning techniques. However, even if inductive learning methods work well when handling symbolic attributes, problems arise when considering numerical or numerical-symbolic (num/symb) attributes. This problem can be solved by introducing tools from fuzzy set th...
In this paper we report on how using sample-dependent coding schemes can lead to poor results in applying Rissanen's Minimum Description Length (MDL) principle [Ris89]. The MDL principle is one of the many known model selection methods in the field of `machine learning', `statistics', or `inductive inference'. We analyze the experimental results pr...
We present a framework for the study of real-world time-series data using supervised Machine Learning techniques. This methodology has been developed to suit the needs of data monitoring in Intensive Care Unit, where data are highly heterogeneous. It is based on the windowed processing and monitoring of model characteristics, in order to detect cha...
