Abstract
This paper proposes a spiking neural network (SNN) of the mammalian auditory midbrain to achieve binaural multiple sound source localisation. The network is inspired by neurophysiological studies on the organisation of binaural processing in the medial superior olive (MSO), lateral superior olive (LSO) and the inferior colliculus (IC) to achieve a sharp azimuthal localisation of sound sources over a wide frequency range in a reverberant environment. Three groups of artificial neurons are constructed to represent the neurons in the MSO, LSO and IC that are sensitive to interaural time difference (ITD), interaural level difference (ILD) and azimuth angle respectively. The ITD and ILD cues are combined in the IC to estimate the azimuth direction of a sound source. To deal with echo, we propose an inter-inhibited onset network in the IC, which can extract the azimuth information from the direct path sound and avoid the effects of reverberation. Experiments show that the proposed onset cell network can localise two sound sources efficiently taking into account the room reverberation.
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References
Bronkhorst, A., Plomp, R.: Effect of multiple speechlike maskers on binaural speech recognition in normal and impaired hearing. The Journal of the Acoustical Society of America 92, 3132–3139 (1992)
Blauert, J.: Spatial Hearing: The Psychophysics of Human Sound Localization. MIT Press, Cambridge (1997)
Jeffress, L.: A place theory of sound localization. J. Comp. Physiol. Psychol. 41, 35–39 (1948)
Moore, B.: An Introduction to the Psychology of Hearing. Academic Press, San Diego (2003)
Yin, T.: Neural mechanisms of encoding binaural localization cues in the auditory brainstem. Integrative Functions in the Mammalian Auditory Pathway I, 99–159 (2002)
Willert, V., Eggert, J., Adamy, J., Stahl, R., Koerner, E.: A probabilistic model for binaural sound localization. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36(5), 982–994 (2006)
Voutsas, K., Adamy, J.: A biologically inspired spiking neural network for sound source lateralization. IEEE Trans. Neural Networks 18(6), 1785–1799 (2007)
Litovsky, R., Colburn, H., Yost, W., Guzman, S.: The precedence effect. Journal of the Acoustical Society of America 106(4 I), 1633–1654 (1999)
Palomäki, K., Brown, G., Wang, D.: A binaural processor for missing data speech recognition in the presence of noise and small-room reverberation. Speech Communication 43(4), 361–378 (2004)
Sivaramakrishnan, S., Oliver, D.: Distinct K Currents Result in Physiologically Distinct Cell Types in the Inferior Colliculus of the Rat. Journal of Neuroscience 21(8), 2861 (2001)
Slaney, M.: An efficient implementation of the patterson-holdsworth auditory filter bank. Apple Computer Technical Report 35 (1993)
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© 2009 Springer-Verlag Berlin Heidelberg
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Liu, J., Perez-Gonzalez, D., Rees, A., Erwin, H., Wermter, S. (2009). Multiple Sound Source Localisation in Reverberant Environments Inspired by the Auditory Midbrain. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_22
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DOI: https://doi.org/10.1007/978-3-642-04274-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04273-7
Online ISBN: 978-3-642-04274-4
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