| Deep Denoising for Hearing Aid Applications Reduction of unwanted environmental noises is an important
feature of today’s hearing aids, which is why noise
reduction is nowadays included in almost every commercially
available device. The majority of these algorithms,
however, is restricted to the reduction of stationary
noises. Due to the large number of different background
noises in daily situations, it is hard to heuristically
cover the complete solution space of noise reduction
schemes. Deep learning-based algorithms pose a possible
solution to this dilemma, however, they sometimes lack
robustness and applicability in the strict context of
hearing aids.
In this project we investigate several deep learning based
methods for noise reduction under the constraints of modern
hearing aids. This involves a low latency processing as
well as the employing a hearing instrument-grade filter
bank. Another important aim is the robustness of the
developed methods. Therefore, the methods will be applied
to real-world noise signals recorded with hearing
instruments. | Projektleitung:
Prof. Dr.-Ing. habil. Andreas Maier
Beteiligte:
Hendrik Schröter, M. Sc., Prof. Dr.-Ing. Marc Aubreville
Beginn: 31.10.2017
Kontakt:
Schröter, Hendrik
Telefon +49 9131 85 27882, Fax +49 9131 85 27270, E-Mail: hendrik.m.schroeter@fau.de
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Aubreville, Marc ; Ehrensperger, Kai ; Rosenkranz, Tobias ; Graf, Benjamin ; Puder, Henning ; Maier, Andreas: Deep Denoising for Hearing Aid Applications. In: IEEE (Hrsg.) : 16th International Workshop on Acoustic Signal Enhancement (IWAENC) (16th International Workshop on Acoustic Signal Enhancement (IWAENC) Tokyo, Japan 17.09.2018). 2018, S. 361-365. - ISBN 978-1-5386-8151-0
[doi>10.1109/IWAENC.2018.8521369] |