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An Adaptive Neural Mechanism with a Lizard Ear Model for Binaural Acoustic Tracking

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From Animals to Animats 14 (SAB 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9825))

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Abstract

Acoustic tracking of a moving sound source is relevant in many domains including robotic phonotaxis and human-robot interaction. Typical approaches rely on processing time-difference-of-arrival cues obtained via multi-microphone arrays with Kalman or particle filters, or other computationally expensive algorithms. We present a novel bio-inspired solution to acoustic tracking that uses only two microphones. The system is based on a neural mechanism coupled with a model of the peripheral auditory system of lizards. The peripheral auditory model provides sound direction information which the neural mechanism uses to learn the target’s velocity via fast correlation-based unsupervised learning. Simulation results for tracking a pure tone acoustic target moving along a semi-circular trajectory validate our approach. Three different angular velocities in three separate trials were employed for the validation. A comparison with a Braitenberg vehicle-like steering strategy shows the improved performance of our learning-based approach.

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References

  1. Aertsen, A., Johannesma, P., Hermes, D.: Spectro-temporal receptive fields of auditory neurons in the grassfrog. Biol. Cybern. 38(4), 235–248 (1980)

    Article  Google Scholar 

  2. Braitenberg, V.: Vehicles: Experiments in Synthetic Psychology. MIT Press, Bradford Books, Cambridge (1984)

    Google Scholar 

  3. Christensen-Dalsgaard, J., Manley, G.: Directionality of the lizard ear. J. Exp. Biol. 208(6), 1209–1217 (2005)

    Article  Google Scholar 

  4. Christensen-Dalsgaard, J., Tang, Y., Carr, C.: Binaural processing by the gecko auditory periphery. J. Neurophysiol. 105(5), 1992–2004 (2011)

    Article  Google Scholar 

  5. Fletcher, N.: Acoustic Systems in Biology. Oxford University Press, New York (1992)

    Google Scholar 

  6. Fletcher, N., Thwaites, S.: Physical models for the analysis of acoustical systems in biology. Q. Rev. Biophys. 12(1), 25–65 (1979)

    Article  Google Scholar 

  7. Ju, T., Shao, H., Peng, Q.: Tracking the moving sound target based on distributed microphone pairs. In: 2013 10th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 330–334, December 2013

    Google Scholar 

  8. Ju, T., Shao, H., Peng, Q., Zhang, M.: Tracking the moving sound target based on double arrays. In: 2012 International Conference on Computational Problem-Solving (ICCP), pp. 315–319, October 2012

    Google Scholar 

  9. Klopf, A.: A neuronal model of classical conditioning. Psychobiology 16(2), 85–125 (1988)

    Google Scholar 

  10. Kosko, B.: Differential Hebbian learning. AIP Conf. Proc. 151(1), 277–282 (1986)

    Article  Google Scholar 

  11. Kwak, K.: Sound source tracking of moving speaker using multi-channel microphones in robot environments. In: 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 3017–3020, December 2011

    Google Scholar 

  12. Liang, Z., Ma, X., Dai, X.: Robust tracking of moving sound source using multiple model Kalman filter. Appl. Acoust. 69(12), 1350–1355 (2008)

    Article  Google Scholar 

  13. Liang, Z., Ma, X., Dai, X.: Robust tracking of moving sound source using scaled unscented particle filter. Appl. Acoust. 69(8), 673–680 (2008)

    Article  Google Scholar 

  14. Manoonpong, P., Geng, T., Kulvicius, T., Porr, B., Wörgötter, F.: Adaptive, fast walking in a biped robot under neuronal control and learning. PLoS Comput. Biol. 3(7), 1–16 (2007)

    Article  Google Scholar 

  15. Manoonpong, P., Wörgötter, F.: Adaptive sensor-driven neural control for learning in walking machines. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009, Part II. LNCS, vol. 5864, pp. 47–55. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  16. Nakadai, K., Lourens, T., Okuno, H., Kitano, H.: Active audition for humanoid. In: Proceedings of 17th National Conference on Artificial Intelligence (AAAI-2000), pp. 832–839. AAAI (2000)

    Google Scholar 

  17. Ning, F., Gao, D., Niu, J., Wei, J.: Combining compressive sensing with particle filter for tracking moving wideband sound sources. In: 2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1–6, September 2015

    Google Scholar 

  18. Nishie, S., Akagi, M.: Acoustic sound source tracking for a moving object using precise Doppler-shift measurement. In: 2013 Proceedings of the 21st European Signal Processing Conference (EUSIPCO), pp. 1–5, September 2013

    Google Scholar 

  19. Okuno, H., Nakadai, K., Hidai, K.I., Mizoguchi, H., Kitano, H.: Human robot non-verbal interaction empowered by real-time auditory and visual multiple-talker tracking. Adv. Robot. 17(2), 115–130 (2003)

    Article  Google Scholar 

  20. Poor, B., Wörgötter, F.: Fast heterosynaptic learning in a robot food retrieval task inspired by the limbic system. Biosystems 89(1–3), 294–299 (2007). Papers Presented at the 6th International Workshop on Neural Coding

    Article  Google Scholar 

  21. Porr, B., Wörgötter, F.: Strongly improved stability and faster convergence of temporal sequence learning by utilising input correlations only. Neural Comput. 18(6), 1380–1412 (2006)

    Article  MATH  Google Scholar 

  22. Reeve, R., Webb, B.: New neural circuits for robot phonotaxis. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 361(1811), 2245–2266 (2003)

    Article  MathSciNet  Google Scholar 

  23. Shaikh, D., Hallam, J., Christensen-Dalsgaard, J.: From “Ear" to there: a review of biorobotic models of auditory processing in lizards. Biol. Cybern. (2016, in press)

    Google Scholar 

  24. Tsuji, D., Suyama, K.: A moving sound source tracking based on two successive algorithms. In: 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009, pp. 2577–2580, May 2009

    Google Scholar 

  25. Valin, J.M., Michaud, F., Rouat, J.: Robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering. Robot. Auton. Syst. 55(3), 216–228 (2007)

    Article  Google Scholar 

  26. Wever, E.: The Reptile Ear: Its Structure and Function. Princeton University Press, Princeton (1978)

    Google Scholar 

  27. Wolpert, D., Ghahramani, Z., Jordan, M.: An internal model for sensorimotor integration. Science 269(5232), 1880–1882 (1995)

    Article  Google Scholar 

  28. Zhang, L.: Modelling directional hearing in lizards. Ph.D. thesis, Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Embodied AI and Neurorobotics Lab, Centre for BioRobotics, Maersk Mc-Kinney Moeller Institute, University of Southern Denmark, Odense M, Denmark

    Danish Shaikh & Poramate Manoonpong

Authors
  1. Danish Shaikh
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  2. Poramate Manoonpong
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Corresponding author

Correspondence to Danish Shaikh .

Editor information

Editors and Affiliations

  1. Aberystwyth University, Aberystwyth, United Kingdom

    Elio Tuci

  2. Aberystwyth University, Aberystwyth, United Kingdom

    Alexandros Giagkos

  3. Aberystwyth University, Aberystwyth, United Kingdom

    Myra Wilson

  4. University of Southern Denmark, Odense, Denmark

    John Hallam

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Shaikh, D., Manoonpong, P. (2016). An Adaptive Neural Mechanism with a Lizard Ear Model for Binaural Acoustic Tracking. In: Tuci, E., Giagkos, A., Wilson, M., Hallam, J. (eds) From Animals to Animats 14. SAB 2016. Lecture Notes in Computer Science(), vol 9825. Springer, Cham. https://doi.org/10.1007/978-3-319-43488-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-43488-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43487-2

  • Online ISBN: 978-3-319-43488-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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