Sebastian Braun

Signal Processing · 2020–2023

A researcher in the Science corpus: 9 papers; trusts 27 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 123 citations into it; #2,251 of 7,572 by trust here, #1,370 by citations
• Artificial Intelligence — 59 citations into it; #25,110 of 35,345 by trust here, #14,438 by citations
• Speech & Audio — 43 citations into it; #1,027 of 1,076 by trust here, #980 by citations
• Cognitive Neuroscience — 37 citations into it
• Computational Mechanics — 36 citations into it; #4,545 of 10,001 by trust here, #4,749 by citations
• Biomedical Engineering — 12 citations into it; #7,774 of 10,734 by trust here, #4,948 by citations
• Electrical and Electronic Engineering — 8 citations into it

Whom Sebastian Braun trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Jean-Marc Valin
2. Jonathan Le Roux
3. Ilya Loshchilov
4. Kyunghyun Cho
5. Scott Wisdom
6. Chandan K. A. Reddy
7. Emmanuel Vincent
8. Yoshua Bengio
9. John R. Hershey
10. Frank Hutter

Who trusts Sebastian Braun overall

1. Chandan K. A. Reddy
2. Ross Cutler
3. Harishchandra Dubey
4. Tianrui Wang
5. Vishak Gopal
6. Weibin Zhu
7. Babak Naderi
8. Xiang Hao
9. Hannes Gamper

Papers from Sebastian Braun's perspective

Ordered by how strongly the researchers Sebastian Braun trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results (2020) — 460 citations
• ICASSP 2022 Acoustic Echo Cancellation Challenge (2022) — 81 citations
• Interspeech 2021 Deep Noise Suppression Challenge (2021) — 206 citations
• Icassp 2022 Deep Noise Suppression Challenge (2022) — 237 citations
• ICASSP 2023 Deep Noise Suppression Challenge (2023) — 193 citations
• Data Augmentation and Loss Normalization for Deep Noise Suppression (2020) — 90 citations
• Weighted Speech Distortion Losses for Neural-Network-Based Real-Time Speech Enhancement (2020) — 137 citations
• ICASSP 2021 Deep Noise Suppression Challenge (2020) — 161 citations
• The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework (2020) — 91 citations