Using Tensor Factorisation Models to Separate Drums from Polyphonic Music

Authors

Derry Fitzgerald, Technological University DublinFollow
Matt Cranitch, Cork Institute of TechnologyFollow
Eugene Coyle, Technological University DublinFollow

Document Type

Conference Paper

Rights

This item is available under a Creative Commons License for non-commercial use only

Disciplines

Electrical and electronic engineering

Publication Details

Proceedings of the International Conference on Digital Audio Effects (DAFX09), Como, Italy, 2009.

Abstract

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.

Recommended Citation

Fitzgerald, D., Coyle, E. & Cranitch, M. Using Tensor Factorisation Models to Separate Drums from Polyphonic Music, Proceedings of the International Conference on Digital Audio Effects (DAFX09), Como, Italy, 2009.

Funder

Science Foundation Ireland; Enterprise Ireland