Development of a flexible, realistic hearing in noise test environment (R-HINT-E)

  • Authors:

  • Laurel Trainor;Ranil Sonnadara;Karl Wiklund;Jeff Bondy;Shilpy Gupta;Suzanna Becker;Ian C. Bruce;Simon Haykin

  • Affiliations:

  • Department of Psychology, McMaster University, 1280 Main West Street, Hamilton, ON, Canada L8S 4K1;Department of Psychology, McMaster University, 1280 Main West Street, Hamilton, ON, Canada L8S 4K1;Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada;Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada;Department of Psychology, McMaster University, 1280 Main West Street, Hamilton, ON, Canada L8S 4K1;Department of Psychology, McMaster University, 1280 Main West Street, Hamilton, ON, Canada L8S 4K1;Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada;Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada

  • Venue:
  • Signal Processing - Special issue on independent components analysis and beyond
  • Year:
  • 2004

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Abstract

Through the use of DSP chips and multiple microphones, hearing aids now offer the possibility of performing signal-to-noise enhancement. Evaluating different algorithms before they are instantiated on a hearing aid is essential. However, commercially available tests of hearing in noise do not allow for speech perception evaluation with a variety of signals, noise types, signal and noise locations, and reverberation. Here we present a flexible realistic hearing in noise testing environment (R-HINT-E) that involves (1) measuring the impulse responses at microphones placed in the ears of a human head and torso model (KEMAR) from many different locations in real rooms of various dimensions and with various reverberation characteristics, (2) creating a corpus of sentences based on the hearing in noise test recorded in quiet from a variety of talkers, (3) creating "soundscapes" representing the input to the ears (or army of microphones in a hearing aid) by convolving specific sentences or noises with the impulse responses for specific locations in a room, and (4) using psychophysical procedures for measuring reception thresholds for speech under a variety of noise conditions. Preliminary evaluation based on the engineering signal-to-error ratio and on human perceptual tests indicates that the convolved sounds closely match real recordings from the same location in the room. R-HINT-E should be invaluable for the evaluation of hearing aid algorithms, as well as more general signal separation algorithms such as independent components analysis.