Evaluation of feedback-reduction algorithms for hearing aids.

Three adaptive feedback-reduction algorithms were implemented in a laboratory-based digital hearing aid system and evaluated with dynamic feedback paths and hearing-impaired subjects. The evaluation included measurements of maximum stable gain and subjective quality ratings. The continuously adapting CNN algorithm (Closed-loop processing with No probe Noise) provided the best performance: 8.5 dB of added stable gain (ASG) relative to a reference algorithm averaged over all subjects, ears, and vent conditions. Two intermittently adapting algorithms, ONO (Open-loop with Noise when Oscillation detected) and ONQ (Open-loop with Noise when Quiet detected), provided an average of 5 dB of ASG. Subjects with more severe hearing losses received greater benefits: 13 dB average ASG for the CNN algorithm and 7-8 dB average ASG for the ONO and ONQ algorithms. These values are conservative estimates of ASG because the fitting procedure produced a frequency-gain characteristic that already included precautions against feedback. Speech quality ratings showed no substantial algorithm effect on pleasantness or intelligibility, although subjects informally expressed strong objections to the probe noise used by the ONO and ONQ algorithms. This objection was not reflected in the speech quality ratings because of limitations of the experimental procedure. The results clearly indicate that the CNN algorithm is the most promising choice for adaptive feedback reduction in hearing aids.