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  5. Deep Learning based Noise Reduction for Hearing Aids

Deep Learning based Noise Reduction for Hearing Aids

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Deep Learning based Noise Reduction for Hearing Aids

Deep Learning based Noise Reduction for Hearing Aids

(Third Party Funds Single)

Overall project:
Project leader: Hendrik Schröter
Project members: Andreas Maier, Marc Aubreville
Start date: February 1, 2019
End date: January 31, 2023
Acronym:
Funding source: Industrie
URL:

Abstract

 

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.

Publications

  • Aubreville M., Ehrensperger K., Rosenkranz T., Graf B., Puder H., Maier A.:
    Deep Denoising for Hearing Aid Applications
    16th International Workshop on Acoustic Signal Enhancement (IWAENC) (Tokyo, JAPAN, September 17, 2018 - September 20, 2018)
    In: 2018 16TH INTERNATIONAL WORKSHOP ON ACOUSTIC SIGNAL ENHANCEMENT (IWAENC), NEW YORK: 2018
    BibTeX: Download
  • Schröter H., Rosenkranz T., Escalante Banuelos A., Aubreville M., Maier A.:
    CLCNet: Deep learning-based noise reduction for hearing aids using complex linear coding
    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (Barcelona, May 4, 2020 - May 8, 2020)
    In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020
    Open Access: https://arxiv.org/abs/2001.10218
    URL: https://rikorose.github.io/CLCNet-audio-samples.github.io/
    BibTeX: Download
  • Schröter H., Rosenkranz T., Escalante Banuelos A., Zobel P., Maier A.:
    Lightweight Online Noise Reduction on Embedded Devices using Hierarchical Recurrent Neural Networks
    INTERSPEECH 2020 (Shanghai, October 25, 2020 - October 29, 2020)
    In: INTERSPEECH 2020 2020
    Open Access: https://arxiv.org/abs/2006.13067
    URL: https://arxiv.org/abs/2006.13067
    BibTeX: Download
  • Schröter H., Rosenkranz T., Escalante Banuelos A., Maier A.:
    CLC: Complex Linear Coding for the DNS 2020 Challenge
    (2020)
    Open Access: https://arxiv.org/abs/2006.13077
    URL: https://github.com/Rikorose/clc-dns-challenge-2020
    BibTeX: Download
  • Schröter H., Rosenkranz T., Escalante Banuelos A., Maier A.:
    LACOPE: Latency-Constrained Pitch Estimation for Speech Enhancement
    Interspeech 2021 (Brno, August 31, 2021 - September 3, 2021)
    In: Proc. Interspeech 2021 2021
    BibTeX: Download
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Erlangen-Nürnberg

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