Intelligent Hearing Assistance Using Self-Organizing Feature Maps


  • Michael Hodges Oakland University Rochester, MI
  • Mohamed Zohdy Oakland University Rochester, MI



self-organizing feature maps, Artificial Intelligence, machine learning, neural networks, pattern classification


A novel hearing assistance system is proposed which classifies sounds and selectively tunes them according to the needs of the hearing impaired.  This differs from the usual hearing aids available today in that it uses computational intelligence to filter and tune sounds based on real life categorical classifications.  The system can significantly improve audibility for the hearing impaired, including bringing completely inaudible tones into audibility.  For classification a self-organizing feature map is used with a vector of sound features from the joint time-frequency domain.  The map is trained with input sounds until a map of neurons is clustered according to its these.  The resulting map is used to classify new sounds.  Based on this classification, a sound can be tuned to improve audibility for the hearing impaired.  Techniques proposed for audio output include gammatone frequency filtering, Fourier compression, low pass filtering, spectral subtraction, and an original algorithm to choose how to best boost the amplitude of deaf frequencies.


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How to Cite

Hodges, M., & Zohdy, M. (2015). Intelligent Hearing Assistance Using Self-Organizing Feature Maps. Transactions on Engineering and Computing Sciences, 2(6), 40.