Measurement and prediction of the acceptable noise level for single-microphone noise reduction algorithms.

Objective: To measure the acceptable noise level (ANL) with and without noise reduction algorithms (NRAs), and to predict ΔANL, i.e. the difference in acceptable noise level with and without NRAs. Design: The ANL test was applied to three NRAs. Furthermore, the measured ΔANL was predicted using seve...

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Detalles Bibliográficos
Publicado en:International Journal of Audiology Vol. 51; no. 4; pp. 299 - 309
Autores principales: Fredelake, Stefan, Holube, Inga, Schlueter, Anne, Hansen, Martin
Formato: research tables/charts Journal Article
Publicado: Taylor & Francis Ltd Apr2012
Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario:Objective: To measure the acceptable noise level (ANL) with and without noise reduction algorithms (NRAs), and to predict ΔANL, i.e. the difference in acceptable noise level with and without NRAs. Design: The ANL test was applied to three NRAs. Furthermore, the measured ΔANL was predicted using several methods based on either the calculation of the signal-to-noise ratio or correlation methods of the processed signals with an unprocessed reference signal. Study sample: Ten normal-hearing and eleven hearing-impaired subjects accomplished the ANL test. Results: In general, the ANL test could determine an increased acceptance of noise with some NRAs. However, great inter-individual differences also resulted that were attributed to audible distortions when an NRA was used. Prediction of the mean measured DANL was possible, but individual prediction of DANL failed due to inter-individual differences. Mean DANL was predicted more accurately for hearing-impaired subjects when individual hearing loss was taken into account. Conclusions: The ANL test is a suitable tool for measuring the advantage of one NRA. A prediction of the measured individual ΔANL failed. However, mean DANL could be predicted with some methods. Furthermore, the individual hearing loss should be taken into account for a more accurate prediction for hearing-impaired subjects.