Audio, Gesture, and Music Analysis with Machines
Professor of Music and Media
MIT, Media Lab
Computers provide the composer with electronically generated sounds. Even though scientists can analyze audio, gestures and music to control musical systems and sound synthesis parameters, they still provide the artist with limited creative and perceptually meaningful feedback. The data is usually gathered, analyzed, processed, scaled, and used internally for making sounds, or sent out to external devices, e.g. using MIDI. However the mapping process remains simplistic and arbitrary. More reactive, adaptive, and creative musical systems could benefit from the use of machine listening, machine learning, and evolutionary models. The computer could analyze better and convert more intelligently the measured raw physical data, i.e. raw gesture data from sensors, and raw perceptual features from audio, into more musically meaningful, e.g. faster, louder, active, consonant, legato, control parameters. I will explore the history of electronic music processes, and focusing on the audio context, I will describe how new techniques in categorizing and understanding the audio content, e.g. timbre, rhythm, and genre classification, can greatly benefit the musical creation. Better results in the synthesis of music will rely on its proper analysis.
The written requirement for this area will consist of a publishable quality paper to be evaluated by Professor Tod Machover.
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