Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems
The Journal of Machine Learning Research
A discriminative model for polyphonic piano transcription
EURASIP Journal on Applied Signal Processing
Note separation of polyphonic music by energy split
ISPRA'08 Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation
Polyphonic music separation based on the simplified energy splitter
WSEAS Transactions on Signal Processing
A computationally efficient method for polyphonic pitch estimation
EURASIP Journal on Advances in Signal Processing
Basis Decomposition of Motion Trajectories Using Spatio-temporal NMF
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Generative spectrogram factorization models for polyphonic piano transcription
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Prediction and classification of motion trajectories using spatio-temporal NMF
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Multipitch estimation of piano sounds using a new probabilistic spectral smoothness principle
IEEE Transactions on Audio, Speech, and Language Processing
Communications of the ACM
Optimal filter designs for separating and enhancing periodic signals
IEEE Transactions on Signal Processing
Multiple fundamental frequency estimation based on sparse representations in a structured dictionary
Digital Signal Processing
Automatic music transcription: challenges and future directions
Journal of Intelligent Information Systems
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In this paper, we present a graphical model for polyphonic music transcription. Our model, formulated as a dynamical Bayesian network, embodies a transparent and computationally tractable approach to this acoustic analysis problem. An advantage of our approach is that it places emphasis on explicitly modeling the sound generation procedure. It provides a clear framework in which both high level (cognitive) prior information on music structure can be coupled with low level (acoustic physical) information in a principled manner to perform the analysis. The model is a special case of the, generally intractable, switching Kalman filter model. Where possible, we derive, exact polynomial time inference procedures, and otherwise efficient approximations. We argue that our generative model based approach is computationally feasible for many music applications and is readily extensible to more general auditory scene analysis scenarios.