Using tensor factorisation models to separate drums from polyphonic music

Derry FitzGerald; Eugene Coyle; Matt Cranitch
DAFx-2009 - Como
This paper describes the use of Non-negative Tensor Factorisation models for the separation of drums from polyphonic audio. Improved separation of the drums is achieved through the incorporation of Gamma Chain priors into the Non-negative Tensor Factorisation framework. In contrast to many previous approaches, the method used in this paper requires little or no pre-training or use of drum templates. The utility of the technique is shown on real-world audio examples.
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