• Skip navigation
  • Skip to navigation
  • Skip to the bottom
Simulate organization breadcrumb open Simulate organization breadcrumb close
Friedrich-Alexander-Universität Pattern Recognition Lab PRL
  • FAUTo the central FAU website
Suche öffnen
  • Campo
  • StudOn
  • FAUdir
  • Jobs
  • Map
  • Help
Friedrich-Alexander-Universität Pattern Recognition Lab PRL
Navigation Navigation close
  • Lab
    • News
    • Cooperations
    • Join the Pattern Recognition Lab
    • Ph.D. Gallery
    • Contact
    • Directions
  • Team
    • Our Team
    • Former PRL members
  • Research
    • Research Groups
    • Research Projects
    • Publications
    • Competitions
    • Datasets
    • Research Demo Videos
    • Pattern Recognition Blog
    • Beyond the Patterns
  • Teaching
    • Curriculum / Courses
    • Lecture Notes
    • Lecture Videos
    • LME Videos
    • Thesis / Projects
  1. Home
  2. Research
  3. Research Groups
  4. Speech Processing and Language Understanding
  5. Störgeräuschreduzierung für Hörgeräte mittels Deep Learning

Störgeräuschreduzierung für Hörgeräte mittels Deep Learning

In page navigation: Research
  • Beyond the Patterns
  • Competitions
  • Publications
  • Datasets
  • An AI-based framework for visualizing and analyzing massive amounts of 4D tomography data for beamline end users
  • An AI-based framework for visualizing and analyzing massive amounts of 4D tomography data for beamline end users
  • An AI-based framework for visualizing and analyzing massive amounts of 4D tomography data for beamline end users

Störgeräuschreduzierung für Hörgeräte mittels Deep Learning

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
    DOI: 10.1109/iwaenc.2018.8521369
    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
    DOI: 10.1109/icassp40776.2020.9053563
    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
    DOI: 10.21437/interspeech.2020-1131
    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
    DOI: 10.21437/interspeech.2021-633
    BibTeX: Download

Friedrich-Alexander-Universität Erlangen-Nürnberg
Lehrstuhl für Mustererkennung (Informatik 5)

Martensstr. 3
91058 Erlangen
  • Contact
  • Login
  • Intranet
  • Imprint
  • Privacy
  • Accessibility
  • RSS Feed
  • Instagram
  • TikTok
  • Mastodon
  • BlueSky
  • YouTube
  • Facebook
  • Xing
  • LinkedIn
  • Community
  • Threads
Up