Toni Heittola

Signal Processing · 2010–2020

A researcher in the Science corpus: 10 papers; trusts 44 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 68 citations into it; #385 of 7,572 by trust here, #720 by citations
• Artificial Intelligence — 50 citations into it; #21,413 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 40 citations into it; #3,770 of 21,575 by trust here, #6,031 by citations
• Developmental Biology — 9 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 9 citations into it
• Speech & Audio — 8 citations into it

Whom Toni Heittola trusts overall

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

1. Annamaria Mesaros
2. Diederik P. Kingma
3. Tuomas Virtanen
4. Dan Stowell
5. Karol J. Piczak
6. Justin Salamon
7. Geoffrey E. Hinton
8. Emre Çakir
9. Yoshua Bengio
10. Jimmy Ba

Who trusts Toni Heittola overall

1. Annamaria Mesaros
2. Tuomas Virtanen
3. Eduardo Fonseca
4. Qiuqiang Kong
5. Shuyang Zhao
6. Shayan Gharib
7. Justin Salamon
8. Nicolas Turpault
9. Emre Çakir
10. Dan Stowell

Papers from Toni Heittola's perspective

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

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

Their papers

• TUT database for acoustic scene classification and sound event detection (2016) — 660 citations
• Active Learning for Sound Event Detection (2020) — 38 citations
• An Active Learning Method Using Clustering and Committee-Based Sample Selection for Sound Event Classification (2018) — 14 citations
• A multi-device dataset for urban acoustic scene classification (2018) — 434 citations
• Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge (2018) — 313 citations
• DCASE2017 Challenge Setup: Tasks, Datasets and Baseline System (2017) — 506 citations
• Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection (2017) — 616 citations
• Metrics for Polyphonic Sound Event Detection (2016) — 649 citations
• Polyphonic sound event detection using multi label deep neural networks (2015) — 298 citations
• Acoustic event detection in real life recordings (2010) — 331 citations