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User Assisted Separation Using Tensor Factorisations, Derry Fitzgerald
User Assisted Separation Using Tensor Factorisations, Derry Fitzgerald
Conference papers
Recent research has demonstrated that user assisted techniques, where the user provides a ”guide” version of the source to be separated, are capable of giving good sound source separation. Here the user sings or plays along with the target source, and the user input is used to guide the separation towards the source of interest. This is typically done in a factorisation framework, such as non-negative matrix factorisation. Here we extend such approaches to a tensor factorisation framework to deal with multichannel signals. Further, we demonstrate how this framework can be used to improve the output from other user assisted …
Using Tensor Factorisation Models To Separate Drums From Polyphonic Music, Derry Fitzgerald, Matt Cranitch, Eugene Coyle
Using Tensor Factorisation Models To Separate Drums From Polyphonic Music, Derry Fitzgerald, Matt Cranitch, Eugene Coyle
Conference papers
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.