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Dependent Component Analysis Using Time-Frequency Analysis

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Blind Source Separation

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSSIGNAL))

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Abstract

Sparsity is an important property shared by many kinds of signals in numerous practical applications. These signals are sparse to some extent in different representation domains, such as time domain, frequency domain or time-frequency domain. In recent years, sparsity has been widely exploited to solve the problem of underdetermined blind source separation (UBSS), where the number of sources exceeds that of the observed mixtures. In fact, the sparsity assumption can also be satisfied by some dependent source signals. For these signals, it is possible to find a number of areas in some representation domains, where the source signals are not active, that is, signals are sparse in theses areas. The sparsity property provides a possibility for the blind separation of dependent sources. In this chapter, the sparsity of dependent sources in the time-frequency (TF) domain will be exploited to achieve blind source separation, where time-frequency analysis (TFA) will be used as a powerful tool for dependent component analysis (DCA). We will also show that for those non-sparse signals whose auto-source points and cross-source points do not overlap in the TF plane, they can be separated by using TFA if the underdetermined mixing system is known.

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Authors and Affiliations

  1. School of Information Technology, Deakin University, Melbourne, Victoria, Australia

    Yong Xiang

  2. College of Computer Science, Sichuan University, Chengdu, Sichuan, China

    Dezhong Peng

  3. Faculty of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China

    Zuyuan Yang

Authors
  1. Yong Xiang
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  2. Dezhong Peng
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  3. Zuyuan Yang
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Correspondence to Yong Xiang .

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Xiang, Y., Peng, D., Yang, Z. (2015). Dependent Component Analysis Using Time-Frequency Analysis. In: Blind Source Separation. SpringerBriefs in Electrical and Computer Engineering(). Springer, Singapore. https://doi.org/10.1007/978-981-287-227-2_3

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  • DOI: https://doi.org/10.1007/978-981-287-227-2_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-226-5

  • Online ISBN: 978-981-287-227-2

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