Probabilistic Melodic Harmonization
Jean-Francois Paiement, Douglas Eck and Samy Bengio
Proceedings of the 19th Canadian Conference on Artificial Intelligence
We propose a representation for musical chords that allows us to
include domain knowledge in probabilistic models. We then introduce
a graphical model for harmonization of melodies that considers every
structural components in chord notation. We show empirically that
root notes progressions exhibit global dependencies that can be
better captured with a tree structure related to the meter than with
a simple dynamical HMM that concentrates on local dependencies.
However, a local model seems to be sufficient for generating proper
harmonizations when root notes progressions are provided. The
trained probabilistic models can be sampled to generate very
interesting chord progressions given other polyphonic music
components such as melody or root note progressions.