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Issue No.03 - July-September (2011 vol.18)
pp: 78-85
Walter Schulze , Stellenbosch University
Brink van der Merwe , Stellenbosch University
ABSTRACT
<p>By using music written in a certain style as training data, parameters can be calculated for Markov chains and hidden Markov models to capture the musical style of the training data as mathematical models.</p>
INDEX TERMS
algorithmic music composition, hidden Markov models, probabilistic automata
CITATION
Walter Schulze, Brink van der Merwe, "Music Generation with Markov Models", IEEE MultiMedia, vol.18, no. 3, pp. 78-85, July-September 2011, doi:10.1109/MMUL.2010.44
REFERENCES
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2. W. Chai and B. Vercoe, "Folk Music Classification Using Hidden Markov Models," Proc. Int'l Conf. Artificial Intelligence, 2001.
3. J. Triviño-Rodriguez and R. Morales-Bueno, "Using Multiattribute Prediction Suffix Graphs to Predict and Generate Music," Computer Music, vol. 25, no. 3, 2001, pp. 62-79.
4. F. Pachet, "The Continuator: Musical Interaction with Style," New Music Research, vol. 32, no. 3, 2003, pp. 333-341.
5. D. Ron, Y. Singer, and N. Tishby, "The Power of Amnesia: Learning Probabilistic Automata with Variable Memory Length," Machine Learning, vol. 25, no. 2, 1996, pp. 117-149.
6. L. Schwardt, Efficient Mixed-Order Hidden Markov Model Inference, doctoral dissertation, Univ. of Stellenbosch, 2007.
7. P. Dupont, F. Denis, and Y. Esposito, "Links between Probabilistic Automata and Hidden Markov Models: Probability Distributions, Learning Models and Induction Algorithms," Pattern Recognition, vol. 38, no. 9, 2005, pp. 1349-1371.
8. D. Jurafsky and J.H. Martin, Speech and Language Processing, 2nd ed., Prentice Hall, 2008.
9. W. Schulze, "A Formal Language Theory Approach to Music Generation," master's thesis, Univ. of Stellenbosch, 2009.
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