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Non-invasive gesture sensing, physical modeling, machine learning and acoustic actuation for pitched percussion

This thesis explores the design and development of digitally extended, electro- acoustic (EA) pitched percussion instruments, and their use in novel, multi-media performance contexts. The proposed techniques address the lack of expressivity in existing EA pitched percussion systems. The research is interdisciplinary in na- ture, combining Computer Science and Music to form a type of musical human- computer interaction (HCI) in which novel playing techniques are integrated in perfor- mances. Supporting areas include Electrical Engineering- design of custom hardware circuits/DSP; and Mechanical Engineering- design/fabrication of new instruments. The contributions can be grouped into three major themes: 1) non-invasive gesture recognition using sensors and machine learning, 2) acoustically-excited physical mod- els, 3) timbre-recognition software used to trigger idiomatic acoustic actuation. In addition to pitched percussion, which is the main focus of the thesis, application of these ideas to other music contexts is also discussed. / Graduate

Identiferoai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/9345
Date07 May 2018
CreatorsTrail, Shawn
ContributorsTzanetakis, George, Driessen, Peter F. (Peter Frank), Schloss, W. Andrew (Walter Andrew)
Source SetsUniversity of Victoria
LanguageEnglish, English
Detected LanguageEnglish
TypeThesis
Formatapplication/pdf
RightsAvailable to the World Wide Web

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