Dannenberg, R. (1984). An on-line algorithm for real-time accompaniment. In Proceedings of ICMC,
p. 193-198, San Francisco.

Desain, P., & Honing, H. (1991). Quantization of musical time: a connectionist approach. In Todd, P. M., & Loy, D. G. (Eds.), Music and Connectionism., pp. 150–167. MIT Press., Cambridge, Mass.

Desain, P., & Honing, H. (1994). A brief introduction to beat induction. In Proceedings of ICMC,
San Francisco.

Dixon, S., & Cambouropoulos, E. (2000). Beat tracking with musical knowledge. In Horn, W. (Ed.),
Proceedings of ECAI 2000 (14th European Conference on Artificial Intelligence) , Amsterdam.

Doucet, A., & Andrieu, C. (2001). Iterative algorithms for state estimation of jump Markov linear ssssems. IEEE Trans. on Signal Processing, 49 (6), 1216-1227.

Doucet, A., de Freitas, N., & Gordon, N. J. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer-Verlag, New York.

Doucet, A., de Freitas, N., Murphy, K., & Russell, S. (2000a). Rao-Blackwellised particle filtering for dynamic Bayesian networks. In Uncertainty in Artificial Intelligence Doucet, A., Godsill, S., & Andrieu, C. (2000b). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10 (3), 197–208.

Fox, D., Burgard, W., & Thrun, S. (1999).  Markov localization for mobile robots in dynamic environments. Journal of Artificial Intelligence Research (JAIR), 11.

Ghahramani, Z., & Hinton, G. (1998). Variational learning for switching state-space models. Neural Computation, 12 (4), 963–996.

Ghahramani, Z., & Hinton, G. E. (1996). Parameter estimation for linear dynamical systems. (crgtr-96-2). Tech. rep., University of Totronto. Dept. of Computer Science.

Godsill, S., Doucet, A., & West, M. (2001). Maximum a posteriori sequence estimation using Monte Carlo particle filters. Annals of the Institute of Statistical Mathematics, 52 (1), 82–96.

Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-
Gaussian Bayesian state estimation. In IEE Proceedings Part F, Radar and Signal Processing, Vol. 140(2), pp. 107–113.

Goto, M., & Muraoka, Y. (1998). Music understanding at the beat level: Real-time beat tracking for audio signals. In Rosenthal, D. F., & Okuno, H. G. (Eds.), Computational Auditory Scene Analysis.

Grubb, L. (1998). A Probabilistic Method for Tracking a Vocalist. Ph.D. thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA.

Hamanaka, M., Goto, M., Asoh, H., & Otsu, N. (2001). A learning-based quantization: Estimation of onset times in a musical score. In Proceedings of the 5th World Multi-conference on Systemics, Cybernetics and Informatics (SCI 2001), Vol. X, pp. 374-379.

Heijink, H., Desain, P., & Honing, H. (2000). Make me a match: An evaluation of different approaches to score-performance matching. Computer Music Journal, 24 (1), 43–56.

Heskes, T. Zoeter, O. (2002). Expectation propagation for approximate inference in dynamic Bayesian networks. In Proceedings UAI.

Isard, M., & Blake, A. (1996). Contour tracking by stochastic propagation of conditional density.

In ECCV (1), pp. 343–356.

Large, E. W., & Jones, M. R. (1999). The dynamics of attending: How we track time-varying events.

Psychological Review, 106, 119-159.

Liu, J. S., Chen, R., & Logvinenko, T. (2001). A theoretical framework for sequential importance sampling with resaampling. In Doucet, A., de Freitas, N., & Gordon, N. J. (Eds.), Sequential Monte Carlo Methods in Practice, pp. 225-246. Springer Verlag.