Jehan, Tristan: Creating Music by Listening (dissertation)
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Creating Music by Listening
by
Tristan Jehan
Diplˆome d’Ing´enieur en Informatique et T´el´ecommunications
IFSIC, Universit´e de Rennes 1, France, 1997
M.S. Media Arts and Sciences
Massachusetts Institute of Technology, 2000
Submitted to the Program in Media Arts and Sciences,
School of Architecture and Planning,
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
at the
MASSACHUSETTS INSTITUTE OF T ECHNOLOGY
September, 2005
c
Mass achusetts Institute of Technology 2005. All rights reserved.
Author. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Program in Media Arts and Sciences
June 17, 2005
Certified by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tod Machover
Professor of Music and Media
Thesis Sup e rvisor
Accepted by. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Andrew B. Lippman
Chairman, Departmental Committee on Graduate Students
2
Creating Music by Listening
by Tristan Jehan
Submitted to the Program in Media Arts and Sciences,
School of Architecture and Planning, on June 17, 2005,
in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
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 mod-
els 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 in-
tends 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, c ross-s ynthesis or song morphing, and ultimately
the s ynthesis of original pieces.
Thesis supervisor: Tod Machover, D.M.A.
Title: Professor of Music and Media
4
Thesis Committee
Thesis supervisor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tod Machover
Professor of Music and Media
MIT Program in Media Arts and Sciences
Thesis reader. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Peter Cariani
Research Assistant Professor of Physiology
Tufts Medical School
Thesis reader. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Fran¸cois Pachet
Senior Rese archer
Sony Computer Science Laboratory
Thesis reader. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Julius O. Smith III
Associate Professor of Music and (by courtesy) Electrical Engineering
CCRMA, Stanford University
Thesis reader. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Barry Vercoe
Professor of Media Arts and Sciences
MIT Program in Media Arts and Sciences
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Acknowledgments
It goes without saying that this thesis is a collaborative piece of work. Much
like the system presented here draws musical ideas and sounds from multiple
song examples, I personally drew ideas, influences, and inspirations from many
people to whom I am very thankful for:
My committee: Tod Machover, Peter Cariani, Fran¸cois Pachet, Julius O. Smith
III, Barry Vercoe.
My collaborators and friends: Brian, Mary, Hugo, Carla, Cati, Ben, Ali, An-
thony, Jean-Julien, Hedlena, Giordano, Stacie, Shelly, Victor, Bernd, Fr´edo,
Joe, Peter, Marc, Sergio, Joe Paradiso, Glorianna Davenport, Sile O’Modhrain,
Deb Roy, Alan Oppenheim.
My Media Lab group and friends: Adam, David, Rob, Gili, Mike, Jacqueline,
Ariane, Laird.
My friends outside of the Media Lab: Jad, Vincent, Gaby, Erin, Brazilnut, the
Wine and Cheese club, 24 Magazine St., 45 Banks St., Rustica, 1369, Anna’s
Taqueria.
My family: Micheline, Ren´e, C´ecile, Fran¸cois, and Co.
Acknowledgments 7
8 Acknowledgments
“A good composer does not imitate; he steals.”
– Igor Stravinsky
10 Acknowledgments