Chandan K. A. Reddy

Signal Processing · 2017–2022

A researcher in the Science corpus: 12 papers; trusts 25 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 105 citations into it; #1,393 of 7,572 by trust here, #671 by citations
• Artificial Intelligence — 57 citations into it; #14,726 of 35,345 by trust here, #11,921 by citations
• Speech & Audio — 46 citations into it; #599 of 1,076 by trust here, #711 by citations
• Computational Mechanics — 28 citations into it; #3,548 of 10,001 by trust here, #2,203 by citations
• Cognitive Neuroscience — 23 citations into it; #2,882 of 10,852 by trust here, #2,700 by citations
• Electrical and Electronic Engineering — 12 citations into it
• Biomedical Engineering — 9 citations into it; #3,376 of 10,734 by trust here, #4,052 by citations

Whom Chandan K. A. Reddy trusts overall

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

1. Ross Cutler
2. Babak Naderi
3. Sebastian Braun
4. Jean-Marc Valin
5. Hannes Gamper
6. Jort Florent Gemmeke
7. Ebrahim Beyrami
8. Vishak Gopal
9. Tom Ko
10. Andrew Hines

Who trusts Chandan K. A. Reddy overall

1. Ross Cutler
2. Vishak Gopal
3. Harishchandra Dubey
4. Babak Naderi
5. Sebastian Braun
6. Julius Richter
7. Anurag Kumar
8. Alexandre D'efossez
9. Hubert Siuzdak
10. Xue Jiang

Papers from Chandan K. A. Reddy's perspective

Ordered by how strongly the researchers Chandan K. A. Reddy trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• A scalable noisy speech dataset and online subjective test framework (2019) — 186 citations
• The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results (2020) — 460 citations
• Interspeech 2021 Deep Noise Suppression Challenge (2021) — 206 citations
• Icassp 2022 Deep Noise Suppression Challenge (2022) — 237 citations
• Dnsmos P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors (2021) — 419 citations
• Dnsmos: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors (2020) — 579 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
• Intrusive and Non-Intrusive Perceptual Speech Quality Assessment Using a Convolutional Neural Network (2019) — 61 citations
• Non-intrusive Speech Quality Assessment Using Neural Networks (2019) — 114 citations
• An individualized super-Gaussian single microphone Speech Enhancement for hearing aid users with smartphone as an assistive device (2017) — 63 citations