Speech/Non-Speech Classification in Hearing Aids Driven by Tailored Neural Networks

  • Enrique Alexandre
  • Lucas Cuadra
  • Manuel Rosa-Zurera
  • Francisco López-Ferreras
Part of the Studies in Computational Intelligence book series (SCI, volume 83)

This chapter explores the feasibility of using some kind of tailored neural networks to automatically classify sounds into either speech or non-speech in hearing aids. These classes have been preliminary selected aiming at focusing on speech intelligibility and user's comfort. Hearing aids in the market have important constraints in terms of computational complexity and battery life, and thus a set of trade-offs have to be considered. Tailoring the neural network requires a balance consisting in reducing the computational demands (that is the number of neurons) without degrading the classification performance. Special emphasis will be placed on designing the size and complexity of the multilayer perceptron constructed by a growing method. The number of simple operations will be evaluated, to ensure that it is lower than the maximum sustained by the computational resources of the hearing aid.


Hearing Loss Feature Vector Digital Signal Processing Hide Neuron Audio Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Enrique Alexandre
    • 1
  • Lucas Cuadra
    • 1
  • Manuel Rosa-Zurera
    • 1
  • Francisco López-Ferreras
    • 1
  1. 1.Department of Signal Theory and CommunicationsUniversity of AlcaláAlcalá de HenaresSpain

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