PhD thesis defense annoucement - Computational rhythm description

[Fabien Gouyon <fgouyon@xxxxxxxxxx>]

Dear List,

For anybody interested, I would like to announce my PhD dissertation
defense  on November 30th 2005 at 11 am, in the Auditorium of the Pompeu
Fabra University in Barcelona.
If by any chance you happen to be around at that time, you are very
welcome to attend.


TITLE:
A computational approach to rhythm description --- Audio features for the
computation of rhythm periodicity functions and their use in tempo
induction and music content processing

You can find more information (and download the dissertation) at
http://www.iua.upf.es/~fgouyon/thesis/

At http://www.iua.upf.es/~fgouyon/thesis/workshop.html, you can also find
information on a satellite one-day workshop on research at the interface
between music, technology, brain science and psychology that you would
also be welcome to attend.

Best regards,
Fabien Gouyon





ABSTRACT:

This dissertation is about musical rhythm. More precisely, it is concerned
with computer programs that automatically extract rhythmic descriptions
from musical audio signals.

New algorithms are presented for tempo induction, tatum estimation, time
signature determination, swing estimation, swing transformations and
classification of ballroom dance music styles. These algorithms directly
process digitized recordings of acoustic musical signals. The backbones of
these algorithms are rhythm periodicity functions: functions measuring the
salience of a rhythmic pulse as a function of the period (or frequency) of
the pulse, calculated from selected instantaneous physical attributes
(henceforth features) emphasizing rhythmic aspects of sound. These
features are computed at a constant time rate on small chunks (frames) of
audio signal waveforms.

Our algorithms determine tempo and tatum of different genres of music,
with almost constant tempo, with over 80% accuracy if we do not insist on
finding a specific metrical level. They identify time signature with
around 90% accuracy, assuming lower metrical levels are known. They
classify ballroom dance music in 8 categories with around 80% accuracy
when taking nothing but rhythmic aspects of the music into account.
Finally they add (or remove) swing to musical audio signals in a
fully-automatic fashion, while conserving very good sound quality.

>From a more general standpoint, this dissertation substantially
contributes to the field of computational rhythm description
a) by proposing an unifying functional framework;
b) by reviewing the architecture of many existing systems with respect to
individual blocks of this framework;
c) by organizing the first public evaluation of tempo induction
algorithms; and
d) by identifying promising research directions, particularly with respect
to the selection of instantaneous features which are best suited to the
computation of useful rhythm periodicity functions and the strategy to
combine and parse multiple sources of rhythmic information.