Tensor factorization with application to convolutive blind source separation of speech

Sanei, Saeid and Makkiabadi, B. 2010. Tensor factorization with application to convolutive blind source separation of speech. Wang, Wenwu, ed. Machine Audition: Principles, Algorithms and Systems, Hershey, PA: Information Science Reference,, pp. 186-206.

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Abstract

Tensor factorization (TF) is introduced as a powerful tool for solving multi-way problems. As an effective and major application of this technique, separation of sound particularly speech signal sources from their corresponding convolutive mixtures is described and the results are demonstrated. The method is flexible and can easily incorporate all possible parameters or factors into the separation formulation. As a consequence of that fewer assumptions (such as uncorrelatedness and independency) will be required. The new formulation allows further degree of freedom to the original parallel factor analysis (PARAFAC) problem in which the scaling and permutation problems of the frequency domain blind source separation (BSS) can be resolved. Based on the results of experiments using real data in a simulated medium, it has been concluded that compared to conventional frequency domain BSS methods, both objective and subjective results are improved when the proposed algorithm is used.

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Publisher:	Information Science Reference,
ISBN:	9781615209200
Related URLs:	Publisher
Last Modified:	19 Mar 2016 22:47
URI:	https://orca.cardiff.ac.uk/id/eprint/27107

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