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Multi-target pitch tracking of vibrato sources in noise using the GM-PHD filter

Stowell, D and Plumbley, MD Multi-target pitch tracking of vibrato sources in noise using the GM-PHD filter In: Proc. 5th International Workshop on Machine Learning and Music (MML 2012), 2012-06-30 - ?, Edinburgh, Scotland, UK.

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Abstract

Probabilistic approaches to tracking often use single-source Bayesian models; applying these to multi-source tasks is problematic. We apply a principled multi-object tracking implementation, the Gaussian mixture probability hypothesis density filter, to track multiple sources having fixed pitch plus vibrato. We demonstrate high-quality ltering in a synthetic experiment, and nd improved tracking using a richer feature set which captures underlying dynamics. Our implementation is available as open-source Python code.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Stowell, DUNSPECIFIEDUNSPECIFIED
Plumbley, MDm.plumbley@surrey.ac.ukUNSPECIFIED
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:37
Last Modified : 17 May 2017 15:12
URI: http://epubs.surrey.ac.uk/id/eprint/839865

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