Extended Nonnegative Tensor Factorisation Models for Musical Sound Source Separation

Joint Authors

FitzGerald, Derry
Cranitch, Matt
Coyle, Eugene

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2008, Issue 2008 (31 Dec. 2008), pp.1-15, 15 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2008-05-29

Country of Publication

Egypt

No. of Pages

15

Main Subjects

Biology

Abstract EN

Recently, shift-invariant tensor factorisation algorithms have been proposed for the purposes of sound source separation of pitched musical instruments.

However, in practice, existing algorithms require the use of log-frequency spectrograms to allow shift invariance in frequency which causes problems when attempting to resynthesise the separated sources.

Further, it is difficult to impose harmonicity constraints on the recovered basis functions.

This paper proposes a new additive synthesis-based approach which allows the use of linear-frequency spectrograms as well as imposing strict harmonic constraints, resulting in an improved model.

Further, these additional constraints allow the addition of a source filter model to the factorisation framework, and an extended model which is capable of separating mixtures of pitched and percussive instruments simultaneously.

American Psychological Association (APA)

FitzGerald, Derry& Cranitch, Matt& Coyle, Eugene. 2008. Extended Nonnegative Tensor Factorisation Models for Musical Sound Source Separation. Computational Intelligence and Neuroscience،Vol. 2008, no. 2008, pp.1-15.
https://search.emarefa.net/detail/BIM-505107

Modern Language Association (MLA)

FitzGerald, Derry…[et al.]. Extended Nonnegative Tensor Factorisation Models for Musical Sound Source Separation. Computational Intelligence and Neuroscience No. 2008 (2008), pp.1-15.
https://search.emarefa.net/detail/BIM-505107

American Medical Association (AMA)

FitzGerald, Derry& Cranitch, Matt& Coyle, Eugene. Extended Nonnegative Tensor Factorisation Models for Musical Sound Source Separation. Computational Intelligence and Neuroscience. 2008. Vol. 2008, no. 2008, pp.1-15.
https://search.emarefa.net/detail/BIM-505107

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-505107