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Artificial neural network-based channel selection and loudness mapping

R J Wang1, M A Jabri

  • 1Systems Engineering and Design Automation Laboratory, Sydney University Electrical Engineering, Australia.

The Annals of Otology, Rhinology & Laryngology. Supplement
|September 1, 1995
PubMed
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Artificial neural networks improve cochlear implant systems by enhancing loudness mapping and enabling smart channel selection. This technology offers better accuracy and easier implementation for improved hearing experiences.

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Cochlear implant systems rely on effective loudness mapping and channel selection for optimal auditory perception.
  • Current methods may have limitations in adapting to individual user needs and optimizing sound processing.

Purpose of the Study:

  • To introduce artificial neural network (ANN) techniques for advanced loudness mapping and intelligent channel selection in cochlear implants.
  • To evaluate the performance of ANNs in accurately mapping loudness and selecting optimal channels for improved speech intelligibility.

Main Methods:

  • Utilized a multilayer perceptron (MLP) for precise loudness mapping based on individual threshold and comfort levels.
  • Developed a neural network-based system for "smart" channel selection, choosing 6 out of 18 channels.

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  • Trained and tested the system on diverse multispeaker speech databases.

    • Main Results:

    • Demonstrated that a simple MLP architecture achieves high accuracy in loudness mapping.
    • The neural network-based channel selection significantly outperformed existing methods used in leading cochlear implant systems.
    • The proposed ANN approach is readily implementable within cochlear implant speech processors.

    Conclusions:

    • ANNs offer a powerful and efficient solution for enhancing cochlear implant functionality.
    • The developed techniques provide superior loudness mapping and channel selection, leading to improved outcomes for users.
    • This research paves the way for more sophisticated and personalized hearing rehabilitation technologies.