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To reveal the importance of temporal precision in ground truth audio event labels, we collected precise (~0.1 sec resolution) "strong" labels for a portion of the AudioSet dataset.
“Explaining odds ratios,”
M Szumilas, · 2010
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“A dataset and taxonomy for urban sound research,”
J Salamon, C Jacoby, and J Bello, · 2014
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“TUT database for acoustic scene classification and sound event detection,”
A Mesaros, T Heittola, and T Virtanen, · 2016
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“Deep residual learning for image recognition,”
K He, X Zhang, S Ren, and J Sun, · 2016
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“Audio Set: An ontology and human-labeled dataset for audio events,”
J Gemmeke, D Ellis, D Freedman, A Jansen, W Lawrence, C Moore, M Plakal, and M Ritter, · 2017
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“Detection and classification of acoustic scenes and events: Outcome of the DCASE 2016 challenge,”
A Mesaros, T Heittola, E Benetos, P Foster, M Lagrange, T Virtanen, and M Plumbley, · 2017
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“DCASE 2017 challenge setup: Tasks, datasets and baseline system,”
A Mesaros, T Heittola, A Diment, B Elizalde, A Shah, E Vincent, B Raj, and T Virtanen, · 2017
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“Scaper: A library for soundscape synthesis and augmentation,”
J Salamon, D MacConnell, M Cartwright, P Li, and J Bello, · 2017
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“CNN architectures for large-scale audio classification,”
S Hershey, S Chaudhuri, D Ellis, J Gemmeke, A Jansen, C Moore, M Plakal, D Platt, R Saurous, B Seybold, M Slaney, R Weiss, and K Wilson, · 2017
Cited alongside, same era.
“Audio event and scene recognition: A unified approach using strongly and weakly labeled data,”
A Kumar and B Raj, · 2017
Cited alongside, same era.
“Seeing sound: Investigating the effects of visualizations and complexity on crowdsourced audio annotations,”
M Cartwright, A Seals, J Salamon, A Williams, S Mikloska, D MacConnell, E Law, J Bello, and O Nov, · 2017
Cited alongside, same era.
“Revisiting unreasonable effectiveness of data in deep learning era,”
C Sun, A Shrivastava, S Singh, and A Gupta, · 2017
Cited alongside, same era.
“Knowledge concentration: Learning 100k object classifiers in a single CNN,”
J Gao, Z Guo, Z Li, and R Nevatia, · 2017
Cited alongside, same era.
“Sound event detection in domestic environments with weakly labeled data and soundscape synthesis,”
N Turpault, R Serizel, J Salamon, and A Shah, · 2019
Later among the works it cites.
“A deep residual network for large-scale acoustic scene analysis,”
L Ford, H Tang, F Grondin, and J Glass, · 2019
Later among the works it cites.
“Learning sound event classifiers from web audio with noisy labels,”
E Fonseca, M Plakal, D Ellis, F Font, X Favory, and X Serra, · 2019
Later among the works it cites.
“Audio tagging with noisy labels and minimal supervision,”
E Fonseca, M Plakal, F Font, D Ellis, and X Serra, · 2019
Later among the works it cites.
“PANNs: Large-scale pretrained audio neural networks for audio pattern recognition,”
Q Kong, Y Cao, T Iqbal, Y Wang, W Wang, and M Plumbley, · 2020
Later among the works it cites.
“Addressing missing labels in large-scale sound event recognition using a teacher-student framework with loss masking,”
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“Learning from noisy large-scale datasets with minimal supervision,”
A Veit, N Alldrin, G Chechik, I Krasin, A Gupta, and S Belongie, · 2017
Cited alongside, same era.
“Adaptive pooling operators for weakly labeled sound event detection,”
B McFee, J Salamon, and J Bello, · 2018
Cited alongside, same era.
“AudioSet: A large-scale dataset of manually annotated audio events,” https://g.co/audioset
Cited in the paper.
E Fonseca, S Hershey, M Plakal, D Ellis, A Jansen, and C Moore, · 2020
Later among the works it cites.
“Limitations of weak labels for embedding and tagging,”
N Turpault, R Serizel, and E Vincent, · 2020
Later among the works it cites.