Ego Noise Reduction for Hose-Shaped Rescue Robot Combining Independent Low-Rank Matrix Analysis and Multichannel Noise Cancellation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10169)


In this paper, we present an ego noise reduction method for a hose-shaped rescue robot, developed for search and rescue operations in large-scale disasters. It is used to search for victims in disaster sites by capturing their voices with its microphone array. However, ego noises are mixed with voices, and it is difficult to differentiate them from a call for help from a disaster victim. To solve this problem, we here propose a two-step noise reduction method involving the following: (1) the estimation of both speech and ego noise signals from observed multichannel signals by multichannel nonnegative matrix factorization (NMF) with the rank-1 spatial constraint, and (2) the application of multichannel noise cancellation to the estimated speech signal using reference signals. Our evaluations show that this approach is effective for suppressing ego noise.


Rescue robot Tough environment Noise reduction Nonnegative matrix factorization Independent vector analysis Multichannel noise cancellation 



This work was supported by the Japan Science and Technology Agency and the Impulsing Paradigm Change through Disruptive Technologies Program (ImPACT) designed by the Council for Science, Technology and Innovation, and partly supported by SECOM Science and Technology Foundation. We would also like to express our gratitude to Prof. Hiroshi Okuno and Mr. Yoshiaki Bando for providing experimental data.


  1. 1.
    Impulsive Paradigm Change through Disruptive Technologies Program (ImPACT).
  2. 2.
    Namari, H., Wakana, K., Ishikura, M., Konyo, M., Tadokoro, S.: Tube-type active scope camera with high mobility and practical functionality. In: Proceedings of IEEE/RSJ IROS, pp. 3679–3686 (2012)Google Scholar
  3. 3.
    Deleforge, A., Kellerman, W.: Phase-optimized K-SVD for signal extraction from underdetermined multichannel sparse mixtures. In: Proceedings of IEEE ICASSP, pp. 355–359 (2015)Google Scholar
  4. 4.
    Barfuss, H., Kellerman, W.: Improving blind source separation performance by adaptive array geometries for humanoid robots. In: Proceedings of HSCMA (2014)Google Scholar
  5. 5.
    Barfuss, H., Kellerman, W.: An adaptive microphone array topology for target signal extraction with humanoid robots. In: Proceedings of IWAENC, pp. 16–20 (2014)Google Scholar
  6. 6.
    Aichner, R., Zourub, M., Buchner, H., Kellerman, W.: Post-processing for convolutive blind source separation. In: Proceedings of ICASSP (2006)Google Scholar
  7. 7.
    Mae, N., Kitamura, D., Ishimura, M., Yamada, T., Makino, S.: Ego noise reduction for hose-shaped rescue robot combining independent low-rank matrix analysis and noise cancellation. In: Proceedings of APSIPA (2016, to be published)Google Scholar
  8. 8.
    Kitamura, D., Ono, N., Sawada, H., Kameoka, H., Saruwatari, H.: Efficient multichannel nonnegative matrix factorization exploiting rank-1 spatial model. In: Proceedings of ICASSP, pp. 276–280 (2015)Google Scholar
  9. 9.
    Kitamura, D., Ono, N., Sawada, H., Kameoka, H., Saruwatari, H.: Determined blind source separation unifying independent vector analysis and nonnegative matrix factorization. IEEE/ACM Trans. Audio Speech Lang. Process. 24(9), 1626–1641 (2016)CrossRefGoogle Scholar
  10. 10.
    Kim, T., Eltoft, T., Lee, T.-W.: Independent vector analysis: an extension of ICA to multivariate components. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 165–172. Springer, Heidelberg (2006). doi: 10.1007/11679363_21 CrossRefGoogle Scholar
  11. 11.
    Hiroe, A.: Solution of permutation problem in frequency domain ICA, using multivariate probability density functions. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 601–608. Springer, Heidelberg (2006). doi: 10.1007/11679363_75 CrossRefGoogle Scholar
  12. 12.
    Kim, T., Attias, H.T., Lee, S.-Y., Lee, T.-W.: Blind source separation exploiting higher-order frequency dependencies. IEEE Trans. Speech Audio Process. 15(1), 70–79 (2007)CrossRefGoogle Scholar
  13. 13.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by nonnegative matrix factorization. Nature 401, 788–791 (1999)CrossRefGoogle Scholar
  14. 14.
    Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. Proc. NIPS 13, 556–562 (2001)Google Scholar
  15. 15.
    Cichocki, A., Zdunek, R., Phan, A.H., Amari, S.: Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. Wiley, New York (2009)CrossRefGoogle Scholar
  16. 16.
    Ozerov, A., Févotte, C.: Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation. IEEE Trans. ASLP 18(3), 550–563 (2010)Google Scholar
  17. 17.
    Kameoka, H., Yoshioka, T., Hamamura, M., Roux, J., Kashino, K.: Statistical model of speech signals based on composite autoregressive system with application to blind source separation. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds.) LVA/ICA 2010. LNCS, vol. 6365, pp. 245–253. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15995-4_31 CrossRefGoogle Scholar
  18. 18.
    Sawada, H., Kameoka, H., Araki, S., Ueda, N.: Multichannel extensions of non-negative matrix factorization with complex-valued data. IEEE Trans. ASLP 21(5), 971–982 (2013)Google Scholar
  19. 19.
    Murata, N., Ikeda, S., Ziehe, A.: An approach to blind source separation based on temporal structure of speech signals. Neurocomputing 41(14), 1–24 (2001)CrossRefzbMATHGoogle Scholar
  20. 20.
    Ishimura, M., Makino, S., Yamada, T., Ono, N., Saruwatari, H.: Noise reduction using independent vector analysis and noise cancellation for a hose-shaped rescue robot. In: Proceedings of IWAENC (2016)Google Scholar
  21. 21.
    Hänsler, E., Schmidt, G.: Acoustic Echo and Noise Control: A Practical Approach. Wiley, New York (2004)CrossRefGoogle Scholar
  22. 22.
    Vincent, E., Gribonval, R., Févotte, C.: Performance measurement in blind audio source separation. IEEE Trans. ASLP 14, 1462–1469 (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of TsukubaTsukubaJapan
  2. 2.SOKENDAI (The Graduate University for Advanced Studies)HayamaJapan
  3. 3.National Institute of Informatics (NII)Chiyoda-kuJapan
  4. 4.The University of TokyoBunkyo-kuJapan

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