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Cognitive Computation
Erschienen in: Cognitive Computation 2/2013

01.06.2013

Underdetermined Blind Source Separation by Parallel Factor Analysis in Time-Frequency Domain

verfasst von: Liu Yang, Jun Lv, Yong Xiang

Erschienen in: Cognitive Computation | Ausgabe 2/2013

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Abstract

This paper presents a new time-frequency approach to the underdetermined blind source separation using the parallel factor decomposition of third-order tensors. Without any constraint on the number of active sources at an auto-term time-frequency point, this approach can directly separate the sources as long as the uniqueness condition of parallel factor decomposition is satisfied. Compared with the existing two-stage methods where the mixing matrix should be estimated at first and then used to recover the sources, our approach yields better source separation performance in the presence of noise. Moreover, the mixing matrix can be estimated at the same time of the source separation process. Numerical simulations are presented to show the superior performance of the proposed approach to some of the existing two-stage blind source separation methods that use the time-frequency representation as well.
Vorheriger Artikel Quantized Control Design for Coupled Dynamic Networks with Communication Constraints
Nächster Artikel Distributed Interference Alignment Algorithm for Multiple-Input Multiple-Output Networks with Uncoordinated Interference

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Metadaten
Titel
Underdetermined Blind Source Separation by Parallel Factor Analysis in Time-Frequency Domain
verfasst von
Liu Yang
Jun Lv
Yong Xiang
Publikationsdatum
01.06.2013
Verlag
Springer-Verlag
Erschienen in
Cognitive Computation / Ausgabe 2/2013
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-012-9177-9

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