Vishak Gopal
Signal Processing · 2020–2023
A researcher in the Science corpus: 8 papers; trusts 17 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Signal Processing — 114 citations into it; #2,298 of 7,572 by trust here, #1,273 by citations
- Artificial Intelligence — 61 citations into it; #25,868 of 35,345 by trust here, #18,586 by citations
- Speech & Audio — 37 citations into it; #604 of 1,076 by trust here, #711 by citations
- Computational Mechanics — 33 citations into it; #5,571 of 10,001 by trust here, #4,178 by citations
- Cognitive Neuroscience — 29 citations into it; #4,841 of 10,852 by trust here, #3,875 by citations
- Biomedical Engineering — 13 citations into it; #7,870 of 10,734 by trust here, #5,112 by citations
- Electrical and Electronic Engineering — 9 citations into it
Whom Vishak Gopal trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Chandan K. A. Reddy
- Ross Cutler
- Babak Naderi
- Hannes Gamper
- Sebastian Braun
- Jort Florent Gemmeke
- Ebrahim Beyrami
- Jean-Marc Valin
- Yi Luo
- Harishchandra Dubey
Who trusts Vishak Gopal overall
- Chandan K. A. Reddy
- Ross Cutler
- Harishchandra Dubey
- Babak Naderi
- Julius Richter
- Anurag Kumar
- Zalán Borsos
- Yi-Chiao Wu
- Xue Jiang
- Evgeny Kharitonov
Papers from Vishak Gopal's perspective
Ordered by how strongly the researchers Vishak Gopal trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Audio Set: An ontology and human-labeled dataset for audio events
- ImageNet: A large-scale hierarchical image database
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- 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
- 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
- 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
- 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