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Neural system identification model of human sound localization.

C Jin1, M Schenkel, S Carlile

  • 1Department of Physiology, and Computer and Engineering Laboratory, The University of Sydney, New South Wales, Australia. craigj@physiol.usyd.edu.au

The Journal of the Acoustical Society of America
|September 29, 2000
PubMed
Summary

Biological constraints shape human auditory localization. A neural model using head-related transfer functions and frequency division achieved humanlike sound localization performance, highlighting the importance of these constraints.

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Area of Science:

  • Auditory Neuroscience
  • Computational Auditory Neuroscience

Background:

  • Human sound localization relies on directional acoustical cues.
  • Understanding the biological constraints of this process is crucial for accurate modeling.

Purpose of the Study:

  • To investigate the role of biological constraints in human auditory localization.
  • To compare the performance of competing models against human subjects using biologically plausible realism constraints.

Main Methods:

  • Utilized a psychophysical and neural system modeling approach.
  • Derived directional acoustical cues from human head-related transfer functions (HRTFs).
  • Employed the Auditory Image Model for cochlear processing and a time-delay neural network for spatial analysis.

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Main Results:

  • A system model combining cochlear processing and neural networks achieved humanlike sound localization.
  • Frequency division and training with variable bandwidth/center-frequency sounds were key for model performance.
  • Results underscore the relevance of these factors to human sound localization.

Conclusions:

  • Biological realism constraints are integral to accurate auditory localization models.
  • Frequency division and adaptable training parameters significantly influence sound localization accuracy.
  • The developed model provides insights into the neural mechanisms underlying human spatial hearing.