CLCNET: Deep Learning-Based Noise Reduction for Hearing aids using Complex Linear Coding

@article{Schrter2020CLCNETDL,
  title={CLCNET: Deep Learning-Based Noise Reduction for Hearing aids using Complex Linear Coding},
  author={Hendrik Schr{\"o}ter and T. Rosenkranz and Alberto N. Escalante and M. Aubreville and A. Maier},
  journal={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2020},
  pages={6949-6953}
}
  • Hendrik Schröter, T. Rosenkranz, +2 authors A. Maier
  • Published 2020
  • Computer Science, Engineering, Mathematics
  • ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Noise reduction is an important part of modern hearing aids and is included in most commercially available devices. Deep learning-based state-of-the-art algorithms, however, either do not consider real-time and frequency resolution constrains or result in poor quality under very noisy conditions.To improve monaural speech enhancement in noisy environments, we propose CLCNet, a framework based on complex valued linear coding. First, we define complex linear coding (CLC) motivated by linear… Expand
CLC: Complex Linear Coding for the DNS 2020 Challenge
Guided Source Separation

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