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

Stowell, D and Plumbley, MD (2012) Multi-target pitch tracking of vibrato sources in noise using the GM-PHD filter In: 5th International Workshop on Machine Learning and Music (MML 2012), 30 June 2012, 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 (Conference Paper)
Divisions :	Surrey research (other units)
Authors :	Name Email ORCID Stowell, D Plumbley, MD m.plumbley@surrey.ac.uk
Date :	2012
Related URLs :	Publisher
Depositing User :	Symplectic Elements
Date Deposited :	17 May 2017 13:37
Last Modified :	23 Jan 2020 18:43
URI:	http://epubs.surrey.ac.uk/id/eprint/839865

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