Independent vector analysis: Model, applications, challenges

Zhongqiang Luo

doi:10.1016/j.patcog.2023.109376

2023-06-01

PRE

Zhongqiang Luo *

DOI：10.1016/j.patcog.2023.109376

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摘要

En 中文

This paper overviews an appealing unsupervised learning method named independent vector analysis (IVA) for its promising applications, such as in audio/speech signal separation, medical signal processing, remote sensing, video/image processing, wireless communication processing, and so on. As a useful data -driven technique in blind source separation (BSS) field, IVA has played an increasingly vital role in dealing with the problems of convolutive mixture separation, multivariate latent variable analysis and multivari-ate data fusion. IVA extends the conventional independent component analysis (ICA) to multidimensional components, which can result in more available information utilization. Compared with ICA mechanism, IVA is not only to utilize the statistical independence of multivariate signals but also the statistical inner dependency of each multivariate signal. With this generalization, IVA can manipulate some prominent ill-pose issues faced in sensor receiving models and has the advantage to overcome the inherent random permutation ambiguity problem in joint BSS. Motivated by the flexible and versatile technology superi-orities of IVA, this paper concentrates on reviewing the IVA model in details, associated methods briefly, and its potential applications as well as prospects. Moreover, some significant open problems about IVA challenges are also discussed in this paper.(c) 2023 Elsevier Ltd. All rights reserved.

Keyword:

IVA

BSS

ICA

Source priori models

Unsupervised learning

Audio source separation