Evaluation of model-based versus non-parametric monaural noise-reduction approaches for hearing aids.
OBJECTIVE
Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions.


METHODS
Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule.


METHODS
The perceptual investigation was performed with fourteen hearing-impaired subjects.


RESULTS
The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments.


CONCLUSIONS
Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.
Medizinische Physik, Carl von Ossietzky-Universität Oldenburg, Oldenburg, Germany. niklas.harlander@uni-oldenburg.de
Niklas
N
Harlander
Tobias
T
Rosenkranz
Volker
V
Hohmann
D000368:Aged; D000465:Algorithms; D005260:Female; D006310:Hearing Aids; D006319:Hearing Loss, Sensorineural / Q000628:therapy*; D046088:Hearing Loss, Unilateral / Q000628:therapy*; D006801:Humans; D008297:Male; D009622:Noise / Q000517:prevention & control*; D057240:Patient Preference; D013065:Speech Intelligibility; D013067:Speech Perception