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Simulating listener errors in using genetic algorithms for perceptual optimization.

Deniz Başkent1, Brent Edwards

  • 1Starkey Hearing Research Center, 2150 Shattuck Ave., Ste. 408, Berkeley, California 94704, USA.

The Journal of the Acoustical Society of America
|June 8, 2007
PubMed
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Simulations reveal that listener sensitivity and judgment errors significantly impact the performance of genetic algorithms (GAs) for hearing aid and cochlear implant optimization. These human factors can considerably reduce the effectiveness of GA-based perceptual tuning.

Area of Science:

  • Audiology
  • Human Factors Engineering
  • Computational Intelligence

Background:

  • Genetic algorithms (GAs) have been proposed for optimizing hearing aid and cochlear implant features based on subjective listener feedback.
  • Understanding the influence of human factors on GA performance is crucial for reliable perceptual optimization.

Purpose of the Study:

  • To investigate the impact of two key human factors on the outcome of genetic algorithms (GAs) used for perceptual optimization in audiology.
  • To simulate listener variability in sentence intelligibility discrimination and judgment error rates within the GA framework.

Main Methods:

  • Simulations were conducted to model listeners with varying auditory sensitivity and error rates in providing subjective judgments to a GA.
  • The performance of the GA under these simulated human factors was analyzed.

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Main Results:

  • Simulated listener sensitivity in discriminating sentence intelligibility significantly affected GA performance.
  • Simulated errors in listener judgment input also considerably reduced the effectiveness of the GA.
  • Comparison with previous human subject data validated the simulation findings.

Conclusions:

  • Human factors, specifically perceptual sensitivity and judgment accuracy, can substantially degrade the performance of GAs in audiological applications.
  • These findings highlight the need to account for human variability when implementing GA-based optimization for hearing devices.