Abstract
This paper presents a spiking neural network (SNN) for binaural sound source localisation (SSL). The cues used for SSL were the interaural time (ITD) and level (ILD) differences. ITDs and ILDs were extracted with models of the medial superior olive (MSO) and the lateral superior olive (LSO). The MSO and LSO outputs were integrated in a model of the inferior colliculus (IC). The connection weights between the MSO and LSO neurons to the IC neurons were estimated using Bayesian inference. This inference process allowed the algorithm to perform robustly on a robot with ~40,dB of ego-noise. The results showed that the algorithm is capable of differentiating sounds with an accuracy of 15°.
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References
Even, J., Heracleous, P., Ishi, C., Hagita, N.: Multi-modal front-end for speaker activity detection in small meetings. In: International Conference on Intelligent Robots and Systems, pp. 536–541. IEEE (2011)
Liu, J., Perez-Gonzalez, D., Rees, A., Erwin, H., Wermter, S.: A biologically inspired spiking neural network model of the auditory midbrain for sound source localisation. Neurocomputing 74(1-3), 129–139 (2010)
Nix, J., Hohmann, V.: Sound source localization in real sound fields based on empirical statistics of interaural parameters. The Journal of the Acoustical Society of America 119, 463 (2006)
Recanzone, G.H., Sutter, M.L.: The biological basis of audition. Annual Review of Psychology 59, 119–142 (2008)
Rodemann, T., Heckmann, M., Joublin, F., Goerick, C., Scholling, B.: Real-time sound localization with a binaural head-system using a biologically-inspired cue-triple mapping. In: International Conference on Intelligent Robots and Systems, pp. 860–865. IEEE (2006)
Roman, N., Wang, D.L., Brown, G.J.: Speech segregation based on sound localization. The Journal of the Acoustical Society of America 114, 2236–2252 (2003)
Schnupp, J., Nelken, I., King, A.: Auditory neuroscience: Making sense of sound. The MIT Press (2011)
Slaney, M.: An efficient implementation of the patterson-holdsworth auditory filter bank. Tech. rep., Apple Computer, Perception Group (1993)
Voutsas, K., Adamy, J.: A biologically inspired spiking neural network for sound source lateralization. Transactions on Neural Networks 18(6), 1785–1799 (2007)
Willert, V., Eggert, J., Adamy, J., Stahl, R., Körner, E.: A probabilistic model for binaural sound localization. Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36(5), 982–994 (2006)
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Dávila-Chacón, J., Heinrich, S., Liu, J., Wermter, S. (2012). Biomimetic Binaural Sound Source Localisation with Ego-Noise Cancellation. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_31
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DOI: https://doi.org/10.1007/978-3-642-33269-2_31
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