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[1] James D. Hamilton. Time Series Analysis , 1994 .
[2] Jun S. Liu,et al. Mixture Kalman ®lters , 2000 .
[3] R. Shumway,et al. AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM , 1982 .
[4] Guy J. Brown,et al. Computational auditory scene analysis , 1994, Comput. Speech Lang..
[5] Hisashi Tanizaki,et al. Ch. 22. Nonlinear and non-gaussian state-space modeling with monte carlo techniques: A survey and comparative study , 2003 .
[6] Christophe Andrieu,et al. Iterative algorithms for state estimation of jump Markov linear systems , 2001, IEEE Trans. Signal Process..
[7] Belinda Thom,et al. Unsupervised Learning and Interactive Jazz/Blues Improvisation , 2000, AAAI/IAAI.
[8] Stuart J. Russell,et al. Dynamic bayesian networks: representation, inference and learning , 2002 .
[9] Peter Desain,et al. Quantization of musical time: a connectionist approach , 1989 .
[10] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[11] Gareth O. Roberts,et al. Markov‐chain monte carlo: Some practical implications of theoretical results , 1998 .
[12] Barry Vercoe,et al. Structured audio: creation, transmission, and rendering of parametric sound representations , 1998, Proc. IEEE.
[13] Geoffrey E. Hinton,et al. Variational Learning for Switching State-Space Models , 2000, Neural Computation.
[14] Yuval Peres,et al. Decayed MCMC Filtering , 2012, UAI.
[15] H. C. Longuet-Higgins,et al. Mental Processes: Studies in Cognitive Science , 1987 .
[16] Jun S. Liu,et al. Mixture Kalman filters , 2000 .
[17] Petri Toiviainen,et al. An interactive MIDI accompanist , 1998 .
[18] Luke Windsor,et al. Make Me a Match: An Evaluation of Different Approaches to ScorePerformance Matching , 2000, Computer Music Journal.
[19] Emilios Cambouropoulos,et al. From MIDI to Traditional Musical Notation , 2000 .
[20] Ehl Emile Aarts,et al. Statistical cooling : a general approach to combinatorial optimization problems , 1985 .
[21] Rong Chen,et al. A Theoretical Framework for Sequential Importance Sampling with Resampling , 2001, Sequential Monte Carlo Methods in Practice.
[22] Roger B. Dannenberg,et al. An On-Line Algorithm for Real-Time Accompaniment , 1984, ICMC.
[23] Yuval Peres,et al. Decayed MCMC iltering , 2002, UAI 2002.
[24] E. rey,et al. Variational Learning for Swit hing State-Spa e ModelsZoubin GhahramaniGeo , 2006 .
[25] Nando de Freitas,et al. An Introduction to MCMC for Machine Learning , 2004, Machine Learning.
[26] Peter Desain,et al. Foot-tapping: a brief intoduction to beat induction , 1994, ICMC.
[27] Nando de Freitas,et al. Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.
[28] Eric D. Scheirer,et al. Tempo and beat analysis of acoustic musical signals. , 1998, The Journal of the Acoustical Society of America.
[29] Camilo Rueda,et al. Kant: a Critique of Pure Quantification , 1994, ICMC.
[30] Yaakov Bar-Shalom,et al. Estimation and Tracking: Principles, Techniques, and Software , 1993 .
[31] Simon Dixon,et al. Beat Tracking with Musical Knowledge , 2000, ECAI.
[32] G. Casella,et al. Rao-Blackwellisation of sampling schemes , 1996 .
[33] R. Kohn,et al. Markov chain Monte Carlo in conditionally Gaussian state space models , 1996 .
[34] T. Heskes,et al. Expectation propagation for approximate inference in dynamic bayesian networks , 2002, UAI 2002.
[35] Christopher Raphael,et al. A Probabilistic Expert System for Automatic Musical Accompaniment , 2001 .
[36] A. Doucet,et al. Maximum a Posteriori Sequence Estimation Using Monte Carlo Particle Filters , 2001, Annals of the Institute of Statistical Mathematics.
[37] Hisashi Tanizaki. Nonlinear and Non-Gaussian State-Space Modeling with Monte Carlo Techniques : A Survey and Comparative Study , 2000 .
[38] Simon J. Godsill,et al. On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..
[39] Peter Lawrence,et al. Transcribe: A Comprehensive Autotranscription Program , 1993, ICMC.
[40] W. Burgard,et al. Markov Localization for Mobile Robots in Dynamic Environments , 1999, J. Artif. Intell. Res..
[41] Y. Bar-Shalom. Tracking and data association , 1988 .
[42] Yoichi Muraoka,et al. Musical understanding at the beat level: real-time beat tracking for audio signals , 1998 .
[43] Louis P. DiPalma,et al. Music and Connectionism , 1991 .
[44] Roger B. Dannenberg,et al. A probabilistic method for tracking a vocalist , 1998 .
[45] Nando de Freitas,et al. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.
[46] Peter Desain,et al. On tempo tracking: Tempogram Representation and Kalman filtering , 2000, ICMC.
[47] Geoffrey E. Hinton,et al. Parameter estimation for linear dynamical systems , 1996 .
[48] M. Hamanaka. Learning-Based Quantization : Estimation of Onset Times in a Musical Score , 2001 .
[49] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[50] Tom Heskes Onno,et al. Expe tation propagation for approximate inferen e in dynami Bayesian networks , 2002 .
[51] N. Gordon,et al. Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .
[52] N. Metropolis,et al. The Monte Carlo method. , 1949 .
[53] Miller Puckette,et al. Synthetic Rehearsal: Training the Synthetic Performer , 1985, ICMC.
[54] Christopher Raphael. A Mixed Graphical Model for Rhythmic Parsing , 2001, UAI.
[55] Michael Isard,et al. Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.
[56] Peter Desain,et al. Rhythm Quantization for Transcription , 2000, Computer Music Journal.