Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks 5, 157–166 (1994)
Cemgil, A.T.: Bayesian Music Transcription. PhD thesis, Radboud University of Nijmegen (2004)
Eck, D., Schmidhuber, J.: Finding temporal structure in music: Blues improvisation with LSTM recurrent networks. In: Bourlard, H. (ed.) Neural Networks for Signal Processing XII, Proc. 2002 IEEE Workshop, pp. 747–756. IEEE, New York (2002)
Raphael, C., Stoddard, J.: Harmonic analysis with probabilistic graphical models. In: Proceedings of ISMIR 2003 (2003)
Lavrenko, V., Pickens, J.: Polyphonic music modeling with random fields. In: Proceedings of ACM Multimedia, Berkeley, CA (2003)
Sher, C. (ed.): The New Real Book, vol. 1. Sher Music Co. (1988)
Levine, M.: The Jazz Piano Book. Sher Music Co./Advance Music (1990)
Allan, M., Williams, C.K.I.: Harmonising chorales by probabilistic inference. In: Advances in Neural Information Processing Systems, vol. 17 (2004)
Lauritzen, S.L.: Graphical Models. Oxford University Press, Oxford (1996)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society 39, 1–38 (1977)
Cooper, G., Meyer, L.B.: The Rhythmic Structure of Music. The Univ. of Chicago Press (1960)
Paiement, J.F., Eck, D., Bengio, S., Barber, D.: A graphical model for chord progressions embedded in a psychoacoustic space. In: Proceedings of the 22nd International Conference on Machine Learning (2005)
Paiement, J.F., Eck, D., Bengio, S.: A probabilistic model for chord progressions. In: Proceedings of the 6th International Conference on Music Information Retrieval (2005)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer series in statistics. Springer, Heidelberg (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Paiement, JF., Eck, D., Bengio, S. (2006). Probabilistic Melodic Harmonization. In: Lamontagne, L., Marchand, M. (eds) Advances in Artificial Intelligence. Canadian AI 2006. Lecture Notes in Computer Science(), vol 4013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766247_19
Download citation
DOI: https://doi.org/10.1007/11766247_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34628-9
Online ISBN: 978-3-540-34630-2
eBook Packages: Computer ScienceComputer Science (R0)