Abstract
The role of expectation in listening and composing music has drawn much attention in music cognition since about half a century ago. In this paper, we provide a first attempt to model some aspects of musical expectation specifically pertained to short-time and working memories, in an anticipatory framework. In our proposition anticipation is the mental realization of possible predicted actions and their effect on the perception of the world at an instant in time. We demonstrate the model in applications to automatic improvisation and style imitation. The proposed model, based on cognitive foundations of musical expectation, is an active model using reinforcement learning techniques with multiple agents that learn competitively and in collaboration. We show that compared to similar models, this anticipatory framework needs little training data and demonstrates complex musical behavior such as long-term planning and formal shapes as a result of the anticipatory architecture. We provide sample results and discuss further research.
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Allauzen, C., Crochemore, M., Raffinot, M.: Factor oracle: A new structure for pattern matching. In: Bartosek, M., Tel, G., Pavelka, J. (eds.) SOFSEM 1999. LNCS, vol. 1725, pp. 295–310. Springer, Heidelberg (1999)
Assayag, G., Dubnov, S.: Using factor oracles for machine improvisation. Soft. Computing 8-9, 604–610 (2004)
Biles, J.A.: Genjam in perspective: A tentative taxonomy for genetic algorithm music and art systems. Leonardo 36(1), 43–45 (2003)
Butz, M.V., Sigaud, O., Gérard, P.: Internal models and anticipations in adaptive learning systems. In: Butz, M.V., Sigaud, O., Gérard, P. (eds.) Anticipatory Behavior in Adaptive Learning Systems. LNCS (LNAI), vol. 2684, pp. 86–109. Springer, Heidelberg (2003)
Conklin, D.: Music generation from statistical models. In: Proceedings of Symposium on AI and Creativity in the Arts and Sciences, pp. 30–35 (2003)
Conklin, D., Witten, I.: Multiple viewpoint systems for music prediction. In Journal of New. Music Research 24, 51–73 (1995)
Davidsson, P.: A framework for preventive state anticipation. In: Butz, M.V., Sigaud, O., Gérard, P. (eds.) Anticipatory Behavior in Adaptive Learning Systems. LNCS (LNAI), vol. 2684, pp. 151–166. Springer, Heidelberg (2003)
Dubnov, S.: Spectral anticipations. Computer Music Journal (2006)
Dubnov, S., Assayag, G., Lartillot, O., Bejerano, G.: Using machine-learning methods for musical style modeling. IEEE Computer Society 36(10), 73–80 (2003)
Dubnov, S., El-Yaniv, R., Assayag, G.: Universal classification applied to musical sequences. In: Proc. of ICMC, pp. 322–340, Michigan (1998)
Edelman, G.: Neural Darwinism: The Theory of Neuronal Group Selection. Basic Books (1987)
Feder, M., Merhav, N., Gutman, M.: Universal prediction of individual sequences. IEEE Trans. Inform. Theory 38(4), 1258–1270 (July 1992)
Franklin, J. A.: Predicting reinforcement of pitch sequences via lstm and td. In: Proc. of International Computer Music Conference, Miami, Florida (2004)
Hiller, L.A., Isaacson, L.M.: Experimental Music: Composition with an Electronic Computer. McGraw-Hill Book Company, New York (1959)
Huron, D.: Sweet Anticipation: Music and the Psychology of Expectation. MIT Press, Cambridge (2006)
Lefebvre, A., Lecroq, T.: Computing repeated factors with a factor oracle. In: Proc. of the Australasian Workshop On Combinatorial Algorithms
Martin, A., Seroussi, G., Weinberger, J.: Linear time universal coding and time reversal of tree sources via fsm closure. Information Theory, IEEE Transactions on 50(7), 1442–1468 (July 2004)
Meyer, L.B.: Emotion and Meaning in Music. Univ. of Chicago Press, Chicago (1956)
Moore, A., Atkeson, C.: Prioritized sweeping: Reinforcement learning with less data and less real time. Machine Learning 13, 103–130 (1993)
Pachet, F.: The continuator: Musical interaction with style. In: Proc. of International Computer Music Conference, Gotheborg, Sweden (September 2002)
Pearce, M., Conklin, D., Wiggins, G.: Methods for combining statistical models of music. In: Wiil, U.K. (ed.) CMMR 2004. LNCS, vol. 3310, pp. 295–312. Springer, Heidelberg (2005)
Ron, D., Singer, Y., Tishby, N.: The power of amnesia: Learning probabilistic automata with variable memory length. Machine Learning 25(2-3), 117–149 (1996)
Rosen, R.: Anticipatory Systems of IFSR International Series on Systems Science and Engineering, vol. 1. Pergamon Press, Oxford (1985)
Saffran, J.R., Johnson, E.K., Aslin, R.N., Newport, E.L.: Statistical learning of tonal sequences by human infants and adults. cognition. Cognition 70, 27–52 (1999)
Saul, L.K., Jordan, M.I.: Mixed memory markov models: Decomposing complex stochastic processes as mixtures of simpler ones. Machine Learning 37(1), 75–87 (1999)
Snyder, B.: Music and Memory: An Introduction. MIT Press, New York (2000)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Uchibe, E., Doya, K.: Competitive-cooperative-concurrent reinforcement learning with importance sampling. In: Proc. of International Conference on Simulation of Adaptive Behavior: From Animals and Animats, pp. 287–296 (2004)
Xenakis, I.: Formalized Music. University of Indiana Press (1971)
Ziv, J., Lempel, A.: Compression of individual sequences via variable-rate coding. IEEE Transactions on Information Theory 24(5), 530–536 (1978)
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Cont, A., Dubnov, S., Assayag, G. (2007). Anticipatory Model of Musical Style Imitation Using Collaborative and Competitive Reinforcement Learning. In: Butz, M.V., Sigaud, O., Pezzulo, G., Baldassarre, G. (eds) Anticipatory Behavior in Adaptive Learning Systems. ABiALS 2006. Lecture Notes in Computer Science(), vol 4520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74262-3_16
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DOI: https://doi.org/10.1007/978-3-540-74262-3_16
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