Polyphonic music modeling with random fields
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A graphical model for chord progressions embedded in a psychoacoustic space
ICML '05 Proceedings of the 22nd international conference on Machine learning
MySong: automatic accompaniment generation for vocal melodies
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Connection Science - Music, Brain, Cognition
Probabilistic models for melodic prediction
Artificial Intelligence
Tuning of a knowledge-driven harmonization model for tonal music
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
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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.