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Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio.
“Roc graphs: Notes and practical considerations for researchers,”
T. Fawcett, · 2004
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“Retrieval evaluation with incomplete information,”
C. Buckley and E. M. Voorhees, · 2004
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“Clear evaluation of acoustic event detection and classification systems,”
A. Temko, R. Malkin, C. Zieger, D. Macho, C. Nadeu, and M. Omologo, · 2006
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“Imagenet: A large-scale hierarchical image database,”
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, · 2009
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“Machine hearing: An emerging field [exploratory dsp],”
R. F. Lyon, · 2010
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“Acoustic event detection in real life recordings,”
A. Mesaros, T. Heittola, A. Eronen, and T. Virtanen, · 2010
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“Real-world acoustic event detection,”
X. Zhuang, X. Zhou, M. A. Hasegawa-Johnson, and T. S. Huang, · 2010
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“Rectified linear units improve restricted boltzmann machines,”
V. Nair and G. E. Hinton, · 2010
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“Imagenet classification with deep convolutional neural networks,”
A. Krizhevsky, I. Sutskever, and G. E. Hinton, · 2012
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“Introducing the knowledge graph: things, not strings,” 2012,
A. Singhal, · 2012
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“An exemplar-based nmf approach to audio event detection,”
J. F. Gemmeke, L. Vuegen, P. Karsmakers, B. Vanrumste, et al., · 2013
Cited alongside, same era.
“Very deep convolutional networks for large-scale image recognition,”
K. Simonyan and A. Zisserman, · 2014
Cited alongside, same era.
“Large-scale video classification with convolutional neural networks,”
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, · 2014
Cited alongside, same era.
“Adam: A method for stochastic optimization,”
D. Kingma and J. Ba, · 2014
Cited alongside, same era.
“Dropout: a simple way to prevent neural networks from overfitting.,”
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, · 2014
Cited alongside, same era.
“TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015,
M. Abadi et al., · 2015
Later among the works it cites.
“Batch normalization: Accelerating deep network training by reducing internal covariate shift,”
S. Ioffe and C. Szegedy, · 2015
Later among the works it cites.
“Deep convolutional neural networks and data augmentation for acoustic event detection,”
N. Takahashi, M. Gygli, B. Pfister, and L. Van Gool, · 2016
Closest in time.
“Recurrent neural networks for polyphonic sound event detection in real life recordings,”
G. Parascandolo, H. Huttunen, and T. Virtanen, · 2016
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“Trecvid 2016: Evaluating video search, video event detection, localization, and hyperlinking,”
G. Awad, J. Fiscus, M. Michel, D. Joy, W. Kraaij, A. F. Smeaton, G. Quéenot, M. Eskevich, R. Aly, and R. Ordelman, · 2016
Closest in time.
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C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, · 2015
Cited alongside, same era.
“Deep residual learning for image recognition,”
K. He, X. Zhang, S. Ren, and J. Sun, · 2015
Cited alongside, same era.
“Activitynet: A large-scale video benchmark for human activity understanding,”
B. G. Fabian Caba Heilbron, Victor Escorcia and J. C. Niebles, · 2015
Cited alongside, same era.
“Convolutional, long short-term memory, fully connected deep neural networks,”
T. N. Sainath, O. Vinyals, A. Senior, and H. Sak, · 2015
Cited alongside, same era.
“Beyond short snippets: Deep networks for video classification,”
J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, · 2015
Cited alongside, same era.
“TUT database for acoustic scene classification and sound event detection,”
A. Mesaros, T. Heittola, and T. Virtanen, · 2016
Closest in time.
“Cp-jku submissions for dcase-2016: A hybrid approach using binaural i-vectors and deep convolutional neural networks,”
H. Eghbal-Zadeh, B. Lehner, M. Dorfer, and G. Widmer, · 2016
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“Audio event detection using weakly labeled data,”
A. Kumar and B. Raj, · 2016
Closest in time.
“Audio Set: An ontology and human-labeled dartaset for audio events,”
J. F. Gemmeke, D. P. W. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, · 2017
Closest in time.