Tracking and data association
Musical understanding at the beat level: real-time beat tracking for audio signals
Computational auditory scene analysis
Mental Processes: Studies in Cognitive Science
Mental Processes: Studies in Cognitive Science
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A Mixed Graphical Model for Rhythmic Parsing
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Unsupervised Learning and Interactive Jazz/Blues Improvisation
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A probabilistic method for tracking a vocalist
A probabilistic method for tracking a vocalist
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Rhythm Quantization for Transcription
Computer Music Journal
Make Me a Match: An Evaluation of Different Approaches to Score Performance Matching
Computer Music Journal
Variational Learning for Switching State-Space Models
Neural Computation
Expectation propagation for approximate inference in dynamic bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Iterative algorithms for state estimation of jump Markov linearsystems
IEEE Transactions on Signal Processing
Key, Chord, and Rhythm Tracking of Popular Music Recordings
Computer Music Journal
A Review of Automatic Rhythm Description Systems
Computer Music Journal
Fast particle smoothing: if I had a million particles
ICML '06 Proceedings of the 23rd international conference on Machine learning
Machine learning system for estimating the rhythmic salience of sounds
International Journal of Knowledge-based and Intelligent Engineering Systems - Selected papers from the KES2004 conference
Towards rhythmic analysis of human motion using acceleration-onset times
NIME '07 Proceedings of the 7th international conference on New interfaces for musical expression
A supervised classification algorithm for note onset detection
EURASIP Journal on Applied Signal Processing
Particle filtering applied to musical tempo tracking
EURASIP Journal on Applied Signal Processing
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Signal-to-score music transcription using graphical models
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
CTPPL: a continuous time probabilistic programming language
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Real-time audio-to-score alignment using particle filter for coplayer music robots
EURASIP Journal on Advances in Signal Processing - Special issue on musical applications of real-time signal processing
Automatic music transcription: challenges and future directions
Journal of Intelligent Information Systems
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We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Exact computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval.