PhD thesis on music transcription

[Taylan Cemgil <cemgil@xxxxxxxxxxxxxx>]

Dear list,

My PhD thesis and other papers on music transcription are available from
http://carol.science.uva.nl/~cemgil/papers.html.

Best,
A. Taylan Cemgil

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Title:
Bayesian Music Transcription

Institute:
Radboud University Nijmegen, the Netherlands

Keywords:
Graphical models,
Dynamic Bayesian Networks,
Switching State Space models,
Monte Carlo simulation,
Music Transcription,
Rhythm Quantization,
Tempo Tracking,
Polyphonic Pitch Tracking


Abstract :

Music transcription refers to extraction of a human readable and
interpretable description from a recording of a music performance. The
final goal is to implement a program that can automatically infer a
musical notation that lists the pitch levels of notes and
corresponding score positions in any arbitrary acoustical input. However,
in this full generality, music transcription stays yet as a hard
problem and arguably requires simulation of a
human level intelligence.  On the other hand, under some realistic
assumptions, a practical engineering solution is possible by an
interplay of scientific knowledge from cognitive science, musicology,
musical acoustics and computational techniques from artificial
intelligence, machine learning and digital signal processing. In this
context, the aim of this thesis is to integrate this vast amount of
prior knowledge in a consistent and transparent computational
framework and to demonstrate the feasibility of such an approach in
moving us closer to a practical solution to music transcription.

In this thesis, we approach music transcription as a statistical
inference problem where given a signal, we search for a score that is
consistent with the encoded music. In this context, we identify three
subproblems: Rhythm Quantization, Tempo Tracking and Polyphonic Pitch
Tracking.  For each subproblem, we define a probabilistic generative
model, that relates the observables (i.e. onsets or audio signal) with
the underlying score.  Conceptually, the transcription task is then to
``invert'' this generative model by using the Bayes Theorem and
to estimate the most likely score.