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Canonical Correlation Analysis Using for DOA Estimation of Multiple Audio Sources

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Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

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Abstract

In this paper we study direction of arrival (DOA) estimation of multiple audio sources by canonical correlation analysis (CCA), which is based on a sparse linear arrays. This array is composed of two separated subarrays. From the receiving data set, we can obtain the separate components by CCA. After a simple correlation, time difference can be obtained, and then we can compute the azimuth of different audio sources. The important contribution of this new estimation method is that it can reduce the effect of inter-sensor spacing to DOA estimation and the computation burden is light. Simulation result confirms the validity and practicality of the proposed approach. Results of DOA estimation are more accurate and stable based on this new method.

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Author information

Authors and Affiliations

  1. Naval University of Engineering, Wuhan, 430033, China

    Gaoming Huang

  2. Department of Radio Engineering, Southeast University, Nanjing, 210096, China

    Gaoming Huang, Luxi Yang & Zhenya He

Authors
  1. Gaoming Huang
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  2. Luxi Yang
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  3. Zhenya He
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Editor information

Editors and Affiliations

  1. Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, E08034, Barcelona, Spain

    Joan Cabestany

  2. Department of Computer Architecture and Computer Technology, University of Granada,  

    Alberto Prieto

  3. Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Francisco Sandoval

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© 2005 Springer-Verlag Berlin Heidelberg

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Huang, G., Yang, L., He, Z. (2005). Canonical Correlation Analysis Using for DOA Estimation of Multiple Audio Sources. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_105

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  • DOI: https://doi.org/10.1007/11494669_105

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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