Creating music by listening
Name
228858708-MIT.pdf
Description
Full printable version
Size
38.69 MB
Format
Adobe PDF
Checksum (MD5)
d3b5ea04f87ac5ddd4b5b7366ff4d4f6
Author(s)
Jehan, Tristan, 1974-
Advisor(s)
Tod Machover.
Date Issued
2005
Publisher
Massachusetts Institute of Technology
Abstract
Machines have the power and potential to make expressive music on their own. This thesis aims to computationally model the process of creating music using experience from listening to examples. Our unbiased signal-based solution models the life cycle of listening, composing, and performing, turning the machine into an active musician, instead of simply an instrument. We accomplish this through an analysis-synthesis technique by combined perceptual and structural modeling of the musical surface, which leads to a minimal data representation. We introduce a music cognition framework that results from the interaction of psychoacoustically grounded causal listening, a time-lag embedded feature representation, and perceptual similarity clustering. Our bottom-up analysis intends to be generic and uniform by recursively revealing metrical hierarchies and structures of pitch, rhythm, and timbre. Training is suggested for top-down un-biased supervision, and is demonstrated with the prediction of downbeat. This musical intelligence enables a range of original manipulations including song alignment, music restoration, cross-synthesis or song morphing, and ultimately the synthesis of original pieces.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.
Includes bibliographical references (p. 127-139).
Subjects
Architecture. Program in Media Arts and Sciences.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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