Hendrik Schröter

Orcid: 0000-0003-3147-3014

According to our database1, Hendrik Schröter authored at least 12 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

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Links

On csauthors.net:

Bibliography

2023
Deep Multi-Frame Filtering for Hearing Aids.
Proceedings of the 24th Annual Conference of the International Speech Communication Association, 2023

DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement.
Proceedings of the 24th Annual Conference of the International Speech Communication Association, 2023

2022
Low Latency Speech Enhancement for Hearing Aids Using Deep Filtering.
IEEE ACM Trans. Audio Speech Lang. Process., 2022

Deepfilternet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio.
Proceedings of the 17th International Workshop on Acoustic Signal Enhancement, 2022

Deepfilternet: A Low Complexity Speech Enhancement Framework for Full-Band Audio Based On Deep Filtering.
Proceedings of the IEEE International Conference on Acoustics, 2022

2021
LACOPE: Latency-Constrained Pitch Estimation for Speech Enhancement.
Proceedings of the 22nd Annual Conference of the International Speech Communication Association, Interspeech 2021, Brno, Czechia, August 30, 2021

2020
Ubicomp Digital 2020 - Handwriting classification using a convolutional recurrent network.
CoRR, 2020

CLC: Complex Linear Coding for the DNS 2020 Challenge.
CoRR, 2020

Lightweight Online Noise Reduction on Embedded Devices Using Hierarchical Recurrent Neural Networks.
Proceedings of the 21st Annual Conference of the International Speech Communication Association, 2020

CLCNET: Deep Learning-Based Noise Reduction for Hearing aids using Complex Linear Coding.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019
Deep Representation Learning for Orca Call Type Classification.
Proceedings of the Text, Speech, and Dialogue - 22nd International Conference, 2019

Segmentation, Classification, and Visualization of Orca Calls Using Deep Learning.
Proceedings of the IEEE International Conference on Acoustics, 2019


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