Gestural musical interfaces using real time machine learning

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dc.contributor.author Dasari, Sai Sandeep
dc.date.accessioned 2018-11-19T19:18:33Z
dc.date.available 2018-11-19T19:18:33Z
dc.date.issued 2018-12-01
dc.identifier.uri http://hdl.handle.net/2097/39341
dc.description.abstract We present gestural music instruments and interfaces that aid musicians and audio engineers to express themselves efficiently. While we have mastered building a wide variety of physical instruments, the quest for virtual instruments and sound synthesis is on the rise. Virtual instruments are essentially software that enable musicians to interact with a sound module in the computer. Since the invention of MIDI (Musical Instrument Digital Interface), devices and interfaces to interact with sound modules like keyboards, drum machines, joysticks, mixing and mastering systems have been flooding the music industry. Research in the past decade gone one step further in interacting through simple musical gestures to create, shape and arrange music in real time. Machine learning is a powerful tool that can be smartly used to teach simple gestures to the interface. The ability to teach innovative gestures and shape the way a sound module behaves unleashes the untapped creativity of an artist. Timed music and multimedia programs such as Max/MSP/Jitter along with machine learning techniques open gateways to embodied musical experiences without physical touch. This master's report presents my research, observations and how this interdisciplinary field of research could be used to study wider neuroscience problems like embodied music cognition and human-computer interactions. en_US
dc.language.iso en_US en_US
dc.subject Machine Learning en_US
dc.subject Gesture Recognition en_US
dc.subject Music Technology en_US
dc.subject Neuroscience en_US
dc.subject Audio Engineering en_US
dc.subject Support Vector Machines en_US
dc.title Gestural musical interfaces using real time machine learning en_US
dc.type Report en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Computer Science en_US
dc.description.advisor William H. Hsu en_US
dc.date.published 2018 en_US
dc.date.graduationmonth December en_US


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