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Hendrik Schröter

4 accepted papers

2025

DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids

ICASSP 2025accepted

The DeepFilterNet (DFN) architecture was recently proposed as a deep learning model suited for hearing aid devices. Despite its competitive performance on numerous benchmarks, it still follows a ‘one-size-fits-all’ approach, which aims to train a single, monolithic architecture that generalises acro…

Cited by 0SourceScholar
2022

Deepfilternet: A Low Complexity Speech Enhancement Framework for Full-Band Audio Based On Deep Filtering

ICASSP 2022accepted

Complex-valued processing has brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the process is based on a time-frequency (TF) mask which is applied to a noisy spectrogram, while complex masks (CM) are usually preferred over real-valued masks due to their…

Cited by 0SourceScholar
2020

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

ICASSP 2020accepted

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…

Cited by 0SourceScholar
2019

Segmentation, Classification, and Visualization of Orca Calls Using Deep Learning

ICASSP 2019accepted

Audiovisual media are increasingly used to study the communication and behavior of animal groups, e.g. by placing microphones in the animals habitat resulting in huge datasets with only a small amount of animal interactions. The Orcalab has recorded orca whales since 1973 using stationary underwater…

Cited by 0SourceScholar