Romain Serizel

Signal Processing · 2019–2020

A researcher in the Science corpus: 5 papers; trusts 16 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 43 citations into it; #3,548 of 7,572 by trust here, #3,673 by citations
• Computer Vision and Pattern Recognition — 25 citations into it; #17,901 of 21,575 by trust here, #16,367 by citations
• Speech & Audio — 19 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Artificial Intelligence — 13 citations into it

Whom Romain Serizel trusts overall

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

1. Annamaria Mesaros
2. Frederic Font
3. David Snyder
4. Toni Heittola
5. Tuomas Virtanen
6. Nicolas Turpault
7. Justin Salamon
8. Jort Florent Gemmeke
9. Eduardo Fonseca
10. Guoguo Chen

Who trusts Romain Serizel overall

1. Nicolas Turpault
2. Scott Wisdom
3. Eduardo Fonseca
4. Hakan Erdogan
5. D. Ellis
6. Shawn Hershey

Papers from Romain Serizel's perspective

Ordered by how strongly the researchers Romain Serizel 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. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
5. Long Short-Term Memory
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. Audio Set: An ontology and human-labeled dataset for audio events
9. TUT database for acoustic scene classification and sound event detection
10. Gradient-based learning applied to document recognition

Their papers

• What’s all the Fuss about Free Universal Sound Separation Data? (2020) — 96 citations
• Limitations of Weak Labels for Embedding and Tagging (2020) — 11 citations
• Sound Event Detection in Synthetic Domestic Environments (2020) — 112 citations
• Improving Sound Event Detection in Domestic Environments using Sound Separation (2020) — 54 citations
• Sound Event Detection in Domestic Environments with Weakly Labeled Data and Soundscape Synthesis (2019) — 340 citations