A new feature selection method for volume control in direct-learning hearing aid systems

Authors

  • Jin Zhou Dept. of Electrical Engineering, University of Ottawa, 800 King Edward Av., ON KIN 6N5, Canada
  • Hisham Othman Dept. of Electrical Engineering, University of Ottawa, 800 King Edward Av., ON KIN 6N5, Canada
  • Hilmi Dajani Dept. of Electrical Engineering, University of Ottawa, 800 King Edward Av., ON KIN 6N5, Canada
  • Tyseer Aboulnasr Faculty of Applied Science, University of British Columbia, 5000-2332 Main Mall, BC V6T 1Z4, Canada

Keywords:

Audition, Backpropagation, Gain control, Hearing aids, Neural networks, Artificial Neural Network, Feature selection methods, Hearing aid, Octave bands, Speech intelligibility index, Speech signals, Volume controls

Abstract

A study was conducted to demonstrate an artificial neural network (ANN) as a new feature selection method for volume control in direct-learning hearing aid systems. The goal of the study was to derive suitable features that the ANN used to set the volume such that it optimized speech intelligibility. New features were proposed that were based on measures of speech intelligibility, such as the Speech Intelligibility Index (SII) and the Coherence SII. The performance of these features was investigated using a simulator of a hearing aid user. These features were derived from the calculation of the CII in the third-octave bands and they reflected the SNR and energy of the speech signals in the bands weighted by the psychoacoustic characteristics of the listener.

Published

2009-09-01

How to Cite

1.
Zhou J, Othman H, Dajani H, Aboulnasr T. A new feature selection method for volume control in direct-learning hearing aid systems. Canadian Acoustics [Internet]. 2009Sep.1 [cited 2021May7];37(3):132-3. Available from: https://jcaa.caa-aca.ca/index.php/jcaa/article/view/2169

Issue

Section

Proceedings of the Acoustics Week in Canada