Skip to main content
SpringerLink
Log in

A Single Source Point Detection Algorithm for Underdetermined Blind Source Separation Problem

  • Conference paper
  • First Online:
Advanced Hybrid Information Processing (ADHIP 2018)

Included in the following conference series:

Abstract

To overcome the traditional disadvantages of single source points detection methods in underdetermined blind source separation problem, this paper proposes a novel algorithm to detect single source points for the linear instantaneous mixed model. First, the algorithm utilizes a certain relationship between the time-frequency coefficients and the complex conjugate factors of the observation signal to realize single source points detection. Then, the algorithm finds more time-frequency points that meets the requirements automatically and cluster them by utilizing a clustering algorithm based on the improved potential function. Finally, the estimation of the mixed matrix is achieved by clustering the re-selected single source points. Simulation experiments on linear mixture model demonstrates the efficiency and feasibility for estimating the mixing matrix.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abrard, F., Deville, Y.: A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources. Sig. Process. 85(7), 1389–1403 (2005)

    Article  Google Scholar 

  2. Aissaelbey, A., et al.: Underdetermined blind separation of nondisjoint sources in the time-frequency domain. IEEE Trans. Sig. Process. 55(3), 897–907 (2007)

    Article  MathSciNet  Google Scholar 

  3. Arberet, S., Gribonval, R., Bimbot, F.: A robust method to count and locate audio sources in a stereophonic linear instantaneous mixture. In: Rosca, J., Erdogmus, D., Príncipe, José C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 536–543. Springer, Heidelberg (2006). https://doi.org/10.1007/11679363_67

    Chapter  Google Scholar 

  4. Dong, T., Lei, Y., Yang, J.: An algorithm for underdetermined mixing matrix estimation. Neurocomputing 104, 26 (2013)

    Article  Google Scholar 

  5. Kim, S.G., Chang, D.Y.: Blind separation of speech and sub-Gaussian signals in underdetermined case. In: INTERSPEECH 2004 - ICSLP, International Conference on Spoken Language Processing, Jeju Island, Korea, October 2004. DBLP (2004)

    Google Scholar 

  6. Xie, S., Yang, L., Yang, J.M., et al.: Time-frequency approach to underdetermined blind source separation. IEEE Trans. Neural Netw. Learn. Syst. 23(2), 306–316 (2012)

    Article  MathSciNet  Google Scholar 

  7. Pedersen, M.S., Wang, D.L., Larsen, J., et al.: Two-microphone separation of speech mixtures. IEEE Trans. Neural Netw. 19(3), 475–492 (2008)

    Article  Google Scholar 

  8. Poncelet, F., Kerschen, G., Golinval, J.C., et al.: Output-only modal analysis using blind source separation techniques. Mech. Syst. Sig. Process. 21(6), 2335–2358 (2007)

    Article  Google Scholar 

  9. Zhou, J.T., Zhao, H., Peng, X., Fang, M., Qin, Z., Goh, R.S.M.: Transfer hashing: from shallow to deep. IEEE Trans. Neural Netw. Learn. Syst. https://doi.org/10.1109/tnnls.2018.2827036

    Article  Google Scholar 

  10. Reju, V.G., Koh, S.N., Soon, I.Y.: An algorithm for mixing matrix estimation in instantaneous blind source separation. Sig. Process. 89(9), 1762–1773 (2009)

    Article  Google Scholar 

  11. Ming, X., Xie, S.L., Yu-Li, F.U.: Underdetermined blind delayed source separation based on single source intervals in frequency domain. Acta Electronica Sinica 35(12), 2279–2283 (2007)

    Google Scholar 

  12. Yu, K., Yang, K., Bai, Y.: Estimation of modal parameters using the sparse component analysis based underdetermined blind source separation. Mech. Syst. Signal Process. 45(2), 302–316 (2014)

    Article  Google Scholar 

  13. Tu, Y., Lin, Y., Wang, J., et al.: Semi-supervised learning with generative adversarial networks on digital signal modulation classification. CMC Comput. Mater. Continua 55(2), 243–254 (2018)

    Google Scholar 

  14. Li, Y., et al.: Underdetermined blind source separation based on sparse representation. IEEE Trans. Signal Process. 54(2), 423–437 (2006)

    Article  MathSciNet  Google Scholar 

  15. Zheng, Z., Kumar Sangaiah, A., Wang, T.: Adaptive communication protocols in flying ad-hoc network. IEEE Commun. Mag. 56(1), 136–142 (2018)

    Article  Google Scholar 

  16. Zhao, N., Richard Yu, F., Sun, H., Li, M.: adaptive power allocation schemes for spectrum sharing in interference-alignment-based cognitive radio networks. IEEE Trans. Veh. Technol. 65(5), 3700–3714 (2016)

    Article  Google Scholar 

Download references

Acknowledgment

This work is supported by the National Natural Science Foundation of China (61771154) and the Fundamental Research Funds for the Central Universities (HEUCFG201830).

This paper is also funded by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation.

Meantime, all the authors declare that there is no conflict of interests regarding the publication of this article.

We gratefully thank of very useful discussions of reviewers.

Author information

Authors and Affiliations

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China

    Yu Zhang, Hongxu Tao & Yun Lin

  2. College of Air Traffic Management, Civil Aviation University of China, Tianjin, 300300, China

    Zhaoyue Zhang

Authors
  1. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhaoyue Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongxu Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yun Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yun Lin .

Editor information

Editors and Affiliations

  1. Hunan Normal University, Changsha, China

    Shuai Liu

  2. Hunan City University, Yiyang, China

    Gelan Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, Y., Zhang, Z., Tao, H., Lin, Y. (2019). A Single Source Point Detection Algorithm for Underdetermined Blind Source Separation Problem. In: Liu, S., Yang, G. (eds) Advanced Hybrid Information Processing. ADHIP 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-19086-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-19086-6_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19085-9

  • Online ISBN: 978-3-030-19086-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation