Full Paper at IEEE Xplore

Particle Filtering for High Dimensional Problems

Přednášející: Pete Bunch, Autoři: Pete Bunch, Simon J. Godsill, University of Cambridge, United Kingdom

In this paper, models and algorithms are presented for transcription of pitch and timings in polyphonic music extracts, focusing on the algorithm details of the sequential Markov chain Monte Carlo (MCMC) inference techniques used. The data are decomposed frame-wise into the frequency domain, where a Poisson point process model is used to write a polyphonic pitch likelihood function. A dynamical model is then used to link notes between frames. Inference in the model is carried out via Bayesian filtering using a sequential MCMC algorithm. The filtering procedure is sub-optimal, using some novel assumptions to render the task computationally tractable for large numbers of notes. Initial results with guitar music, both laboratory test data and commercial extracts, show promising performance.

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  Informace o přednášce

Nahráno: 2011-05-26 17:35 - 17:55, Club D
Přidáno: 22. 6. 2011 04:45
Počet zhlédnutí: 19
Rozlišení videa: 1024x576 px, 512x288 px
Délka videa: 0:17:58
Audio stopa: MP3 [6.06 MB], 0:17:58