Dirty tangible interfaces

Savary, Matthieu; Schwarz, Diemo; Pellerin, Denis; Massin, Florence; Jacquemin, Christian; Cahen, Roland

doi:10.1145/2468356.2479592

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…In an audio-audio mosaicing task, the "target" is an example sound that one is trying to resynthesize with the corpus. In cases using interfaces for live controllers [9,12], the mapping between gesture and sound has, until now, taken place in a reduced dimension space. Two or three features are selected as pertinent and projected onto a graphical Cartesian representation.…”

Section: Discussionmentioning

confidence: 99%

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Gesture-Timbre Space: Multidimensional Feature Mapping Using Machine Learning and Concatenative Synthesis

Zbyszynski

Donato

Visi

et al. 2021

Lecture Notes in Computer Science

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This paper presents a method for mapping embodied gesture, acquired with electromyography and motion sensing, to a corpus of small sound units, organised by derived timbral features using concatenative synthesis. Gestures and sounds can be associated directly using individual units and static poses, or by using a sound tracing method that leverages our intuitive associations between sound and embodied movement. We propose a method for augmenting corporal density to enable expressive variation on the original gesture-timbre space.

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Section: Discussionmentioning

confidence: 99%

“…Savary et al [9,10] created Dirty Tangible Interfaces, a typology of user interfaces that favour the production of very rich and complex sounds using CataRT. Interfaces can be constantly evolved, irreversibly, by di↵erent performers at the same time.…”

Section: Related Workmentioning

confidence: 99%

Gesture-Timbre Space: Multidimensional Feature Mapping Using Machine Learning and Concatenative Synthesis

Zbyszynski

Donato

Visi

et al. 2021

Lecture Notes in Computer Science

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“…An inspiration for our approach is the concept of timbre space (Grey 1977;Wessel 1979;McAdams 1999), which proposes a multidimensional space of audio features as a The resulting method, corpus-based concatenative synthesis (CBCS) (Schwarz 2006(Schwarz , 2007, is based on audio content descriptor analysis of any number of pre-existing or live-recorded sounds, and synthesis is guided by selection and playback of sound segments from the database, matching user-chosen sound characteristics. It has been used in various contexts of music composition (Einbond et al 2009(Einbond et al , 2016(Einbond et al , 2021, live performance (Schwarz 2012), sound design, and installation (Savary et al 2012). It allows exploration of a corpus of sounds interactively by composing paths in a multi-dimensional timbre space model, and thus to create timbral evolutions while maintaining the richness and detail of the original sounds.…”

Section: Machine Listening and Corpus-based Sound Synthesismentioning

confidence: 99%

Embodying Spatial Sound Synthesis with AI in Two Compositions for Instruments and 3-D Electronics

Einbond,

Carpentier,

Schwarz

et al. 2022

Computer Music Journal

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The situated spatial presence of musical instruments has been well studied in the fields of acoustics and music perception research, but so far it has not been the focus of human-AI interaction. We respond critically to this trend by seeking to reembody interactive electronics using data derived from natural acoustic phenomena. Two musical works, composed for human soloist and computer-generated live electronics, are intended to situate the listener in an immersive sonic environment in which real and virtual sources blend seamlessly. To do so, we experimented with two contrasting reproduction setups: a surrounding Ambisonic loudspeaker dome and a compact spherical loudspeaker array for radiation synthesis. A large database of measured radiation patterns of orchestral instruments served as a training set for machine learning models to control spatially rich 3-D patterns for electronic sounds. These are exploited during performance in response to live sounds captured with a spherical microphone array and used to train computer models of improvisation and to trigger corpus-based spatial synthesis. We show how AI techniques are useful to utilize complex, multidimensional, spatial data in the context of computer-assisted composition and human-computer interactive improvisation.

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CoMo.education: Collective and embodied storytelling in kindergarten using a gesture-sound interactive system

Voillot,

Matuszewski,

Chevrier

et al. 2024

Proceedings of the 9th International Conference on Movement and Computing

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Dirty tangible interfaces

Cited by 3 publications

References 1 publication

Gesture-Timbre Space: Multidimensional Feature Mapping Using Machine Learning and Concatenative Synthesis

Gesture-Timbre Space: Multidimensional Feature Mapping Using Machine Learning and Concatenative Synthesis

Embodying Spatial Sound Synthesis with AI in Two Compositions for Instruments and 3-D Electronics

CoMo.education: Collective and embodied storytelling in kindergarten using a gesture-sound interactive system

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