ARTICLE cemgil-kappen-barber-2004/IDIAP A Generative Model for Music Transcription Cemgil, A. T. Kappen, B. Barber, David EXTERNAL https://publications.idiap.ch/attachments/papers/2005/pianoroll_tsap_final.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/barber:rr05-89 Related documents IEEE Transactions on Speech and Audio Processing 2004 Accepted for publication 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 modelling 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. REPORT barber:rr05-89/IDIAP A Generative Model for Music Transcription Cemgil, A. T. Kappen, B. Barber, David EXTERNAL https://publications.idiap.ch/attachments/reports/2005/barber-idiap-rr-05-89.pdf PUBLIC Idiap-RR-89-2005 2005 IDIAP Accepted to IEEE Transactions on Speech and Audio Processing 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 modelling 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.