Multisensory Plucked Instrument Modeling in Unity3D: From Keytar to Accurate String Prototyping

Autor: Razvan Paisa, Stefania Serafin, Federico Fontana, Roberto Ranon
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Computer science
stringed musical instruments
Multisensory feedback
Stringed musical instruments
02 engineering and technology
Virtual reality
multisensory feedback
lcsh:Technology
01 natural sciences
GeneralLiterature_MISCELLANEOUS
lcsh:Chemistry
Software
Human–computer interaction
0103 physical sciences
General Materials Science
Set (psychology)
lcsh:QH301-705.5
010301 acoustics
Instrumentation
Musical haptics
Haptic technology
Fluid Flow and Transfer Processes
lcsh:T
business.industry
Process Chemistry and Technology
String (computer science)
General Engineering
musical haptics
021001 nanoscience & nanotechnology
lcsh:QC1-999
Computer Science Applications
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
Dynamics (music)
virtual reality
Guitar
lcsh:Engineering (General). Civil engineering (General)
0210 nano-technology
business
Robotic arm
lcsh:Physics
Zdroj: Applied Sciences
Volume 10
Issue 4
Fontana, F, Paisa, R, Ranon, R & Serafin, S 2020, ' Multisensory plucked instrumentmodeling in unity3D : From Keytar to accurate string prototyping ', Applied Sciences (Switzerland), vol. 10, no. 4, 1452 . https://doi.org/10.3390/app10041452
Applied Sciences, Vol 10, Iss 4, p 1452 (2020)
ISSN: 2076-3417
DOI: 10.3390/app10041452
Popis: Keytar is a plucked guitar simulation mockup developed with Unity3D that provides auditory, visual, and haptic feedback to the player through a Phantom Omni robotic arm. Starting from a description of the implementation of the virtual instrument, we discuss our ongoing work. The ultimate goal is the creation of a set of software tools available for developing plucked instruments in Unity3D. Using such tools, sonic interaction designers can efficiently simulate plucked string prototypes and realize multisensory interactions with virtual instruments for unprecedented purposes, such as testing innovative plucked string interfaces or training machine learning algorithms with data about the dynamics of the performance, which are immediately accessible from the machine.
Databáze: OpenAIRE