Multiple Viewpoint Systems: Time Complexity and the Construction of Domains for Complex Musical Viewpoints in the Harmonization Problem

Journal of New Music Research

Volumn 42, Issue 3, 2013, Pages 237-266

  Whorley, Raymond P ^a   Wiggins, Geraint A ^b   Rhodes, Christophe ^a   Pearce, Marcus T ^b  

^a UNIVERSITY OF LONDON   (United Kingdom)

^b QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

harmonization; machine composition; machine learning; multiple viewpoints; representation

Indexed keywords

EID: 84887084895     PISSN: 09298215     EISSN: 17445027     Source Type: Journal    
DOI: 10.1080/09298215.2013.831457     Document Type: Article

References (36)

1. Aha, D. W., & Bankert, R. L. (1996). A comparative evaluation of sequential feature selection algorithms. In D. Fisher & H. J. Lenz (Eds.), Learning from data: AI and statistics V (pp. 199-206). New York: Springer.
2. Allan, M. (2002). Harmonising chorales in the style of Johann Sebastian Bach (Master's thesis). School of Informatics, University of Edinburgh.
3. Allan, M., & Williams, C. K. I. (2005). Harmonising chorales by probabilistic inference. In L. K. Saul, Y. Weiss & L. Bottou (Eds.), Advances in neural information processing systems (Vol. 17). Cambridge, MA: MIT Press.
4. Assayag, G., Dubnov, S., & Delerue, O. (1999). Guessing the composer's mind: Applying universal prediction to musical style. In Proceedings of the 1999 International Computer Music Conference (pp. 496-499). San Fransisco: ICMA.
5. Biyikoǧlu, K. M. (2003). A Markov model for chorale harmonization. In R. Kopiez, A. C. Lehmann, I. Wolther & C. Wolf (Eds.), Proceedings of the 5th Triennial European Society for the Cognitive Sciences of Music (ESCOM) Conference, Hanover, Germany, pp. 81-84.
6. Cleary, J. G., & Teahan, W. J. (1997). Unbounded length contexts for PPM. The Computer Journal,40(2/3), 67-75.
7. Cleary, J. G., & Witten, I. H. (1984). Data compression using adaptive coding and partial string matching. IEEE Transactions on Communications, COM-32(4), 396-402.
8. Clement, B. J. (1998). Learning harmonic progression using Markov models (EECS545 Project). Michigan, IL: University of Michigan.
9. Conklin, D. (1990). Prediction and entropy of music (Master's thesis). Department of Computer Science, University of Calgary, Canada.
10. Conklin, D. (2002). Representation and discovery of vertical patterns in music. In: C. Anagnostopoulou, M. Ferrand & A. Smaill (Eds.), Music and artificial intelligence: Proceedings ICMAI 2002, LNAI 2445 (pp. 32-42). Berlin: Springer-Verlag.
11. Conklin, D., & Cleary, J. G. (1988). Modelling and generating music using multiple viewpoints. In Proceedings of the First Workshop on AI and Music, St. Paul, MN, pp. 125-137.
12. Conklin, D., & Witten, I. H. (1995). Multiple viewpoint systems for music prediction. Journal of New Music Research,24(1), 51-73.
13. Corman, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001). Introduction to algorithms (2nd ed.). Cambridge, MA: MIT Press.
14. Dixon, S., & Cambouropoulos, E. (2000). Beat tracking with musical knowledge In W. Horn (Ed.), Proceedings of the 14th Biennial European Conference on Artificial Intelligence (ECAI) (pp. 626-630). Amsterdam: IOS Press.
15. Ebcioǧlu, K. (1988). An expert system for harmonizing four-part chorales. Computer Music Journal,12(3), 43-51.
16. Hild, H., Feulner, J., & Menzel, W. (1992). HARMONET: A neural net for harmonizing chorales in the style of J. S. Bach. In R. P. Lippmann, J. E. Moody and D. S. Touretzky (Eds.), Advances in Neural Information Processing Systems (Vol. 4, pp. 267-274). San Francisco, CA: Morgan Kaufmann.
17. Lafferty, J. D., McCallum, A., & Pereira, F. C. N. (2001). Conditional random fields: Probabalistic models for segmenting and labeling sequence data. In Proceedings of ICML, Williamstown, MA, pp. 282-289.
18. Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. Cambridge, MA: MIT Press.
19. Pachet, F., Ramalho, G., & Carrive, J. (1996). Representing temporal musical objects and reasoning in the MusES system. Journal of New Music Research,25(3), 252-275.
20. Pachet, F., & Roy, P. (1998). Formulating contraint satisfaction problems on part-whole relations: The case of automatic harmonization. In 13th Biennial European Conference on Artificial Intelligence (ECAI) Workshop: Constraint Techniques for Artistic Applications, Brighton, UK.
21. Pearce, M. T. (2005). The construction and evaluation of statistical models of melodic structure in music perception and composition (PhD thesis). Department of Computing, City University, London.
22. Pearce, M. T., Conklin, D., & Wiggins, G. A. (2005). Methods for combining statistical models of music. In: U. K. Wiil (Ed.), Computer Music Modelling and Retrieval (pp. 295-312). Berlin: Springer.
23. Pearce, M.T., Müllensiefen, D., & Wiggins, G.A. (2008). A comparison of statistical and rule-based models of melodic segmentation. In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR), Philadelphia, PA, pp. 89-94.
24. Pearce, M. T., Müllensiefen, D., & Wiggins, G. A. (2010). Melodic grouping in music information retrieval: New methods and applications. In Z. W. Ras & A. Wieczorkowska (Eds.), Advances in music information retrieval. Berlin: Springer.
25. Pearce, M. T., & Wiggins, G. A. (2004). Improved methods for statistical modelling of monophonic music. Journal of New Music Research,33(4), 367-385.
26. Pearce, M. T., & Wiggins, G. A. (2006). Expectation in melody: The influence of context and learning. Music Perception,23(5), 377-405.
27. Pearce, M. T., && Wiggins, G. A. (2007). Evaluating cognitive models of musical composition. In A. Cardoso & G.A. Wiggins (Eds.), Proceedings of the 4th International Joint Workshop on Computational Creativity, London, pp. 73-80.
28. Pearce, M. T., & Wiggins, G. A. (2012). Auditory expectation: The information dynamics of music perception and cognition. Topics in Cognitive Science,4(4), 625-652.
29. Phon-Amnuaisuk, S., Tuson, A., & Wiggins, G. A. (1999). Evolving musical harmonisation. In Proceedings of the Fourth International Conference on Neural Networks and Genetic Algorithms (ICANNGA'99). Slovenia: Springer-Verlag.
30. Phon-Amnuaisuk, S., & Wiggins, G. A. (1999). The four-part harmonisation problem: A comparison between genetic algorithms and a rule-based system. In Proceedings of the AISB'99 Symposium on Musical Creativity, Edinburgh, 6-9 April. London: Society for the Study of Artificial Intelligence and Simulation of Behaviour.
31. Ponsford, D., Wiggins, G. A., & Mellish, C. (1999). Statistical learning of harmonic movement. Journal of New Music Research,28(2), 150-177.
32. Raphael, C., & Stoddard, J. (2003). Harmonic analysis with probabilistic graphical models. In Proceedings of the 4th International Conference on Music Information Retrieval (ISMIR), Baltimore, MD, pp. 177-181.
33. Vaughan Williams, R. (Ed.) (1933). The English hymnal. London: Oxford University Press.
34. Viterbi, A. J. (1967). Error bounds for convolutional codes and an asymptotically optimal decoding algorithm. IEEE Transactions on Information Theory,13, 260-269.
35. Whorley, R. P., Rhodes, C., Wiggins, G. A., & Pearce, M. T. (2013). Harmonising melodies: Why do we add the bass line first? In M. L. Maher, A. Veale & R. Saunders & O. Bown, (Eds.), Proceedings of the Fourth International Conference on Computational Creativity, Sydney, Australia, pp. 79-86.
36. Witten, I. H., & Bell, T. C. (1989). The zero frequency problem: Estimating the probability of novel events in adaptive text compression (Technical Report 89/347/09). Calgary, Alberta, Canada: Department of Computer Science, The University of Calgary.