Neurodynamical Top-Down Processing during Auditory Attention

  • Emili Balaguer-Ballester
  • Abdelhamid Bouchachia
  • Beibei Jiang
  • Susan L. Denham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)


Understanding the neural dynamics underlying the fast discrimination of music and speech in noise is a very challenging task for neurocomputational and speech recognition models. In this paper, we present a model of interacting neural ensembles which includes a top-down modulation of the peripheral system dynamics, based on bottom-up perceptual predictions. This bi-directional processing could enable the detection of sudden changes in the input sounds in noise; advancing in the understanding of how listeners can improve their perception by focusing their attention. Our preliminary work opens the possibility of developing a pioneering class of neurophysiological-based speech processors for cochlear implants and speech recognition devices under degraded conditions.


Neurodynamical models auditory perception cochlear efferent modulation speech recognition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Nahum, M., Nelken, I., Ahissar, M.: Low-Level Information and High-Level Perception: The Case of Speech in Noise. PLoS Biol. 6(5), e126 (2008)Google Scholar
  2. 2.
    Lecouteux, B., Vacher, M., Portet, F.: Distant Speech Recognition in a Smart Home: Comparison of Several Multisource ASRs in Realistic Conditions. In: Interspeech, pp. 2273–2276 (2011)Google Scholar
  3. 3.
    Grossberg, S., Kazerounian, S.: Laminar cortical dynamics of conscious speech perception: A neural model of phonemic restoration using subsequent context in noise. J. Acoust. Soc. Am. 130, 440–460 (2011)CrossRefGoogle Scholar
  4. 4.
    Andersson, S., Yamagishi, J., Clark, R.A.J.: Synthesis and evaluation of conversational characteristics in HMM-based speech synthesis. Speech Communication 54, 175–188 (2012)CrossRefGoogle Scholar
  5. 5.
    Parbery-Clark, A., Strait, D.L., Kraus, N.: Context-dependent encoding in the auditory brainstem subserves enhanced speech-in-noise perception in musicians. Neuropsychologia 49, 3338–3345 (2011)CrossRefGoogle Scholar
  6. 6.
    Balaguer-Ballester, E., Clark, N., Coath, M., Krumbholz, K., Denham, S.L.: Understanding pitch perception as a hierarchical process with top-down modulation. PLoS Comput. Biol. 5, e1000301 (2009), doi:101371/journalpcbi1000301Google Scholar
  7. 7.
    Kiebel, S.J., Daunizeau, J., Friston, K.J.: A Hierarchy of Time-Scales and the Brain. PLoS Comput. Biol. 4(11), e1000209, doi:10.1371/journal.pcbi.1000209Google Scholar
  8. 8.
    Carroll, J., Tiaden, S., Zeng, F.G.: Fundamental frequency is critical to speech perception in noise in combined acoustic and electric hearing. J. Acoust. Soc. Am. 130, 2054–2062 (2011)CrossRefGoogle Scholar
  9. 9.
    Guinan, J.: Olivocochlear efferents: anatomy, physiology, function, and the measurement of efferent effects in humans. Ear & Hearing 27, 589–607 (2006)CrossRefGoogle Scholar
  10. 10.
    Ferry, R.T., Meddis, R.: A computer model of medial efferent suppression in the mammalian auditory system. J. Acoust. Soc. Am. 122, 3519–3526 (2007)CrossRefGoogle Scholar
  11. 11.
    de Boer, J., Thornton, A.R., Krumbholz, K.: What is the role of the medial olivocochlear system in speech-in-noise processing? J. of Neurophysiol. 107, 1301–1312 (2012)CrossRefGoogle Scholar
  12. 12.
    Maison, S., Micheyl, C., Collet, L.: Influence of focused auditory attention on cochlear activity in humans. Psychophysiology 38, 35–40 (2001)CrossRefGoogle Scholar
  13. 13.
    Lopez-Poveda, E.A., Meddis, R.: A human nonlinear cochlear filter bank. J. Acoust. Soc. Am. 110, 3107–3118 (2001)CrossRefGoogle Scholar
  14. 14.
    Balaguer-Ballester, E., Denham, S.L., Meddis, R.: A cascade autocorrelation model of pitch perception. J. Acoust. Soc. Am. 124, 2186–2195 (2008)CrossRefGoogle Scholar
  15. 15.
    Yoshida, J., Hasegawa, H., Kasuga, M.: Absolute threshold of hearing decreased by perceiving a previous sound. Acoust. Sci. & Tech. 28, 385–391 (2007)CrossRefGoogle Scholar
  16. 16.
    Aghajan, H., López-Cózar Delgado, R., Augusto, J.C. (eds.): Human-Centric Interfaces for Ambient Intelligence. Elsevier (2010) ISBN: 978-0-12-374708-2 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Emili Balaguer-Ballester
    • 1
    • 2
  • Abdelhamid Bouchachia
    • 1
  • Beibei Jiang
    • 3
  • Susan L. Denham
    • 3
  1. 1.School of Design, Engineering and ComputingBournemouth UniversityPooleUK
  2. 2.Bernstein Center for Computational Neuroscience Heidelberg-MannheimUniversity of HeidelbergGermany
  3. 3.School of Psychology, Faculty of Science and TechnologyUniversity of PlymouthUK

Personalised recommendations