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

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dc.contributor.authorTrail, Shawn
dc.contributor.supervisorTzanetakis, George
dc.contributor.supervisorDriessen, Peter F. (Peter Frank)
dc.contributor.supervisorSchloss, W. Andrew (Walter Andrew)
dc.date.accessioned2018-05-07T14:35:45Z
dc.date.available2018-05-07T14:35:45Z
dc.date.copyright2018en_US
dc.date.issued2018-05-07
dc.degree.departmentInterdisciplinary Graduate Programen_US
dc.degree.levelDoctor of Philosophy Ph.D.en_US
dc.description.abstractThis 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.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/9345
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectNIMEen_US
dc.subjectDMIen_US
dc.subjectElectronic musicen_US
dc.subjectHCIen_US
dc.subjectComputer musicen_US
dc.subjectPitched Percussionen_US
dc.subjectMusical roboticsen_US
dc.subjectMIRen_US
dc.subjectMachine learningen_US
dc.titleNon-invasive gesture sensing, physical modeling, machine learning and acoustic actuation for pitched percussionen_US
dc.typeThesisen_US

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