LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Wall, Julie; McGinnity, Thomas M.; Maguire, Liam P. (2011)
Publisher: IEEE
Languages: English
Types: Unknown
Subjects:
This paper outlines the development of a crosscorrelation\ud algorithm and a spiking neural network (SNN) for\ud sound localisation based on real sound recorded in a noisy and\ud dynamic environment by a mobile robot. The SNN architecture\ud aims to simulate the sound localisation ability of the\ud mammalian auditory pathways by exploiting the binaural cue\ud of interaural time difference (ITD). The medial superior olive\ud was the inspiration for the SNN architecture which required\ud the integration of an encoding layer which produced\ud biologically realistic spike trains, a model of the bushy cells\ud found in the cochlear nucleus and a supervised learning\ud algorithm. The experimental results demonstrate that\ud biologically inspired sound localisation achieved using a SNN\ud can compare favourably to the more classical technique of\ud cross-correlation.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] D. McAlpine and B. Grothe, “Sound localization and delay lines - do mammals fit the model?” Trends Neurosci, vol. 26, no. 7, pp. 347- 350, 2003.
    • [2] T. C. T. Yin, Integrative Functions in the Mammalian Auditory Pathway. Springer-Verlag, 2002, ch. Neural mechanisms of encoding binaural localization cues in the auditory brainstem, pp. 99-159.
    • [3] D. J. Tollin, “The lateral superior olive: A functional role in sound source localization,” Neuroscientist, vol. 9, no. 2, pp. 127-143, 2003.
    • [4] S. P. Thompson “On the function of the two ears in the perception of space,” Philos Mag, vol. 13, no. 83, pp. 406-416, 1882.
    • [5] L. Rayleigh, “On our perception of sound direction,” Philos Mag, vol. 13, no. 74, pp. 214-232, 1907.
    • [6] M. S. Lewicki. (2006) Sound localization 1. [Online]. Available: http://www.cs.cmu.edu/~lewicki/cpsa/sound-localization1.pdf
    • [7] B. Grothe, “New roles for synaptic inhibition in sound localization,” Nature Rev Neurosci, vol. 4, no. 7, pp. 540-550, 2003.
    • [8] R. M. Burger and E. W. Rubel, “Encoding of interaural timing for binaural hearing,” The Senses: A Comprehensive Reference, vol. 3, pp. 613-630, 2008.
    • [9] P. X. Joris and T. C. T. Yin, “A matter of time: Internal delays in binaural processing,” Trends Neurosci, vol. 30, no. 2, pp. 70-78, 2007.
    • [10] L. A. Jeffress, “A place theory of sound localization,” J. Comparative Physiological Psychology, vol. 41, no. 1, pp. 35-39, 1948.
    • [11] J. K. Moore, “Organization of the human superior olivary complex,” Microsc Res Tech, vol. 51, no. 4, pp. 403-412, 2000.
    • [12] D. C. Fitzpatrick, S. Kuwada and R. Batra, “Transformations in processing interaural time differences between the superior olivary complex and inferior colliculus: beyond the Jeffress model,” Hearing Research, vol. 168, no. 1-2, pp. 79-89, 2002.
    • [13] I. Bazwinsky, H. Hilbig, H. J. Bidmon and R. Ruebsamen, “Characterization of the human superior olivary complex by calcium binding proteins and neurofilament H (SMI-32),” J. Comparative Neurology, vol. 456, no. 3, pp. 292-303, 2003.
    • [14] R. J. Kulesza, “Cytoarchitecture of the human superior olivary complex: Medial and lateral superior olive,” Hearing Research, vol. 225, no. 1-2, pp. 80-90, 2007.
    • [15] J. A. Wall, L. J. McDaid, L. P. Maguire and T. M. McGinnity, “Spiking neuron models of the medial and lateral superior olive for sound localisation,” in IEEE Int. Joint Conf. Neural Networks (IJCNN) (IEEE World Congr. Computational Intelligence), 2008, pp. 2641-2647.
    • [16] B. Glackin, J. A. Wall, T. M. McGinnity, L. P. Maguire and L. J. McDaid, “A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localisation,” Front. Comput. Neurosci, vol. 4, pp. 1-16, 2010.
    • [17] W. Maass, “Networks of spiking neurons: The third generation of neural network models,” Neural Networks, vol. 10, no. 9, pp. 1659- 1671, 1997.
    • [18] H. de Garis, N. E. Nawa, M. Hough and M. Korkin, “Evolving an optimal de/convolution function for the neural net modules of ATR's artificial brain project,” in Proc. IEEE Int. Joint Conf. Neural Networks (IJCNN), 1999, vol. 1, pp. 438-443.
    • [19] B. Schrauwen and J. Van Campenhout, “BSA, a fast and accurate spike train encoding scheme,” in Proc. IEEE Int. Joint Conf. Neural Networks (IJCNN), 2003, vol. 4, pp. 2825-2830.
    • [20] W. Gerstner and W. M. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, 2002.
    • [21] S. M. Bohte, J. N. Kok and H. La Poutre, “Spike-prop: Errorbackpropagation for networks of spiking neurons,” in Proc. European Symp. Artificial Neural Networks (ESANN), 2000.
    • [22] R. Legenstein, C. Naeger and W. Maass, “What can a neuron learn with spike-timing-dependent plasticity?” Neural Computation, vol. 17, no. 11, pp. 2337-2382, 2005.
    • [23] J. Hörnstein, M. Lopes, J. Santos-Victor and F. Lacerda, “Sound localization for humanoid robots - building audio-motor maps based on the HRTF,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), 2006, pp. 1170-1176.
    • [24] E. Berglund and J. Sitte, “Sound source localisation through active audition,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), 2005, pp. 653-658.
    • [25] F. Keyrouz and K. Diepold, “A novel biologically inspired neural network solution for robotic 3D sound source sensing,” Soft Comput., vol. 12, no. 7, pp. 721-729, 2008.
    • [26] N. M. Kwok, J. Buchholz, G. Fang and J. Gal, “Sound source localization: Microphone array design and evolutionary estimation,” in Proc. IEEE Int. Conf. Ind. Technology (ICIT), 2006, pp. 281-286.
    • [27] J. Liu, D. Perez-Gonzalez, A. Rees, H. Erwin and S. Wermter, “A biomimetic spiking neural network of the auditory midbrain for mobile robot sound localisation in reverberant environments,” in Proc. IEEE Int. Joint Conf. Neural Networks (IJCNN), 2009, pp. 1855-1862.
    • [28] J. C. Murray, H. R. Erwin and S. Wermter, “Robotic sound-source localisation architecture using cross-correlation and recurrent neural networks,” Neural Networks, vol. 22, no. 2, pp. 173-189, 2009.
    • [29] W. G. Gardner and K. D. Martin, “HRTF measurements of a KEMAR,” J Acoust Soc Am, vol. 97, pp. 3907-3908, 1995.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article