MOLITVA - Composition for Voice, Live Electronics, Pointing-At Glove Device and 3-D Setup of Speakers (original) (raw)
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williambrent.conflations.com
The flexibility of current hardware and software has made the mapping of relationships between a sound's parameters and physical source of control a relatively trivial task. Consequently, the endeavor of sophisticated digital instrument design has been accessible to the creative community for several years, which has resulted in a host of new instruments that explore a variety of physical mappings. The emphasis on physicality exhibited by so-called "gestural controllers" stands in contrast to the practice of conventional laptop performance. While the laptop computer is certainly a digital musical instrument, its associated performance practice is often criticized based on a perceived lack of physicality. This paper examines motivations behind the foregrounding of gesture in computer-based performance. Critical theory and neuroscience research are drawn upon in order to consider ways in which the desire for connections between sound and motion amount to more than mere fascination with virtuosity.
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R e p r i n t f r o m : T r e n d s i n G e s t u r a l C o n t r o l o f M u s i c , M . M . W a n d e r l e y a n d M . B a t t i e r , e d s . © 2 0 0 0 , I r c a m -C e n t r e P o m p i d o u
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Proceedings of the 2014 International Workshop on Movement and Computing - MOCO '14, 2014
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finger_music-like_gestures_CMMR2013.pdf
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The Intangible Cultural Heritage (ICH) implies gestural knowledge and skills in performing arts, such as music, and its preservation and transmission is a worldwide challenge according to UNESCO. This paper presents an ongoing research that aims at the development of a computer vision methodology for the recognition of music-like complex hand and finger gestures performed in space. This methodology can contribute both to the analysis of classical music playing schools, such as the European and the Russian, and to the finger gesture control of sound as a new interface for musical expression. An implementation of a generic method for building body subpart classification model applied in musical gestures is presented. A robust classification model from a reduced training dataset, as well as a method for spatial aggregation of the classification results, which provides a confidence measure on each hand subpart location is developed. A 80% pixel-wise classification accuracy and 95% ponctual subpart location accuracy is achieved when musical finger gestures with a semi-closed hand are performed in front of the camera and the rotation around camera axis is not too important.