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Weakly Labelled learning has garnered lot of attention in recent years due to its potential to scale Sound Event Detection (SED) and is formulated as Multiple Instance Learning (MIL) problem.
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F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research , vol. 12, pp. 2825–2830, 2011
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L. Deng, G. Hinton, and B. Kingsbury, “New types of deep neural network learning for speech recognition and related applications: an overview,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing , 2013, pp. 8599–8603
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D. Stowell, D. Giannoulis, E. Benetos, M. Lagrange, and M. D. Plumbley, “Detection and classification of acoustic scenes and events,” IEEE Transactions on Multimedia , vol. 17, no. 10, pp. 1733–1746, 2015
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K. J. Piczak, “Environmental sound classification with convolutional neural networks,” in 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP) , 2015, pp. 1–6
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2017
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S.-Y. Chou, J.-S. R. Jang, and Y.-H. Yang, “Framecnn : A weakly-supervised learning framework for frame-wise acoustic event detection and classification,” 2017
2017
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T. Su, J. Liu, and Y. Yang, “Weakly-supervised audio event detection using event-specific gaussian filters and fully convolutional networks,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2017, pp. 791–795
2017
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2017
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2015
Cited alongside, same era.
A. Mesaros, T. Heittola, and T. Virtanen, “Tut database for acoustic scene classification and sound event detection,” in 2016 24th European Signal Processing Conference (EUSIPCO) , 2016, pp. 1128–1132
2016
Cited alongside, same era.
A. Kumar and B. Raj, “Audio event detection using weakly labeled data,” in Proceedings of the 24th ACM International Conference on Multimedia , ser. MM ’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 1038–1047. [Online]. Available: https://doi.org/10.1145/2964284.2964310
2016
Cited alongside, same era.
K. Choi, G. Fazekas, and M. Sandler, “Automatic tagging using deep convolutional neural networks,” 06 2016
2016
Cited alongside, same era.
A. Kolesnikov and C. H. Lampert, “Seed, expand and constrain: Three principles for weakly-supervised image segmentation,” in Computer Vision – ECCV 2016 , B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds. Cham: Springer International Publishing, 2016, pp. 695–711
2016
Cited alongside, same era.
K. Choi, G. Fazekas, and M. B. Sandler, “Automatic tagging using deep convolutional neural networks,” in ISMIR , 2016
2016
Cited alongside, same era.
E. Çakır, G. Parascandolo, T. Heittola, H. Huttunen, and T. Virtanen, “Convolutional recurrent neural networks for polyphonic sound event detection,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 25, no. 6, pp. 1291–1303, 2017
2017
Cited alongside, same era.
S.-Y. Tseng, J. Li, Y. Wang, J. Szurley, F. Metze, and S. Das, “Multiple instance deep learning for weakly supervised audio event detection,” 12 2017
2017
Cited alongside, same era.
J. F. Gemmeke, D. P. W. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, “Audio set: An ontology and human-labeled dataset for audio events,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2017, pp. 776–780
2017
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B. McFee, J. Salamon, and J. P. Bello, “Adaptive pooling operators for weakly labeled sound event detection,” IEEE/ACM Trans. Audio, Speech and Lang. Proc. , vol. 26, no. 11, p. 2180–2193, Nov. 2018. [Online]. Available: https://doi.org/10.1109/TASLP.2018.2858559
2018
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Y. Xu, Q. Kong, W. Wang, and M. D. Plumbley, “Large-scale weakly supervised audio classification using gated convolutional neural network,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2018, pp. 121–125
2018
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Q. Kong, Y. Xu, W. Wang, and M. D. Plumbley, “Audio set classification with attention model: A probabilistic perspective,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2018, pp. 316–320
2018
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S. Santurkar, D. Tsipras, A. Ilyas, and A. Mądry, “How does batch normalization help optimization?” in Proceedings of the 32nd International Conference on Neural Information Processing Systems , ser. NIPS’18. Red Hook, NY, USA: Curran Associates Inc., 2018, p. 2488–2498
2018
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2018
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2018
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Q. Kong, Y. Xu, I. Sobieraj, W. Wang, and M. D. Plumbley, “Sound event detection and time–frequency segmentation from weakly labelled data,” IEEE/ACM Trans. Audio, Speech and Lang. Proc. , vol. 27, no. 4, p. 777–787, Apr. 2019. [Online]. Available: https://doi.org/10.1109/TASLP.2019.2895254
2019
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Z. Ren, Q. Kong, J. Han, M. D. Plumbley, and B. W. Schuller, “Attention-based atrous convolutional neural networks: Visualisation and understanding perspectives of acoustic scenes,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 56–60
2019
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2019
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M. Ravanelli, J. Zhong, S. Pascual, P. Swietojanski, J. Monteiro, J. Trmal, and Y. Bengio, “Multi-task self-supervised learning for robust speech recognition,” 01 2020
2020
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