Evaluation of model-based versus non-parametric monaural noise-reduction approaches for hearing aids

Abstract 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 im...

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Vydané v:International journal of audiology Ročník 51; číslo 8; s. 627 - 639
Hlavní autori: Harlander, Niklas, Rosenkranz, Tobias, Hohmann, Volker
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Informa Healthcare 01.08.2012
Taylor & Francis
Predmet:
Aged
Algorithms
Distortion
Female
Hearing Aids
hearing impairment
Hearing Loss, Sensorineural - therapy
Hearing Loss, Unilateral - therapy
Humans
Intelligibility
Listening
Male
model-based
Noise
Noise - prevention & control
Noise reduction
non-stationary noise
Patient Preference
Prototypes
Scaling
Signals
Speech
Speech Intelligibility
Speech Perception
ISSN:1499-2027, 1708-8186, 1708-8186
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Shrnutí:Abstract 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. Design: 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. Study sample: 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. Conclusion: Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.
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ISSN:1499-2027
1708-8186
1708-8186
DOI:10.3109/14992027.2012.684405