Probabilistic Melodic Harmonization

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.

• Type: Conference
• Year: 2006
• Where: AI
• Authors: Jean-François Paiement, Douglas Eck, Samy Bengio
• Keywords: AI 2006, Artificial Intelligence, Probabilistic Models, Root Note Progressions, Root Notes Progressions