2021

DESED-FL and URBAN-FL: Federated Learning Datasets for Sound Event Detection

Johnson, David S., Lorenz, Wolfgang, Taenzer, Michael et al.

Understand

Research on sound event detection (SED) in environmental settings has seen increased attention in recent years.

The large amounts of (private) domestic or urban audio data needed raise significant logistical and privacy concerns.
The inherently distributed nature of these tasks, make federated learning (FL) a promising approach to take advantage of largescale data while mitigating privacy issues.
While FL has also seen increased attention recently, to the best of our knowledge there is no research towards FL for SED.

Built on

K. Hsieh, A. Phanishayee, O. Mutlu, and P. B. Gibbons, “The Non-IID Data Quagmire of Decentralized Machine Learning,” 2019. [Online]. Available: http://arxiv.org/abs/1910.00189
Original
1910
Earlier work this paper cites.
J. Snoek, H. Larochelle, and R. P. Adams, “Practical Bayesian Optimization of Machine Learning Algorithms,” in NeurIPS , Lake Tahoe, Nevada, 2012, pp. 2951–2959
2012
Earlier work this paper cites.
J. Snoek, H. Larochelle, and R. P. Adams, “Practical Bayesian Optimization of Machine Learning Algorithms,” in NeurIPS , Lake Tahoe, Nevada, 2012, pp. 2951–2959
2012
Earlier work this paper cites.
J. Salamon, C. Jacoby, and J. P. Bello, “A dataset and taxonomy for urban sound research,” in 22nd ACM International Conference on Multimedia (ACM-MM’14) , Orlando, FL, USA, 11 2014, pp. 1041–1044
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
A. Dang, T. H. Vu, and J. Wang, “A survey of deep learning for polyphonic sound event detection,” in 2017 International Conference on Orange Technologies (ICOT) , 2017, pp. 75–78
2017
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics , 2017
2017
Earlier work this paper cites.
Y. Lin, S. Han, H. Mao, Y. Wang, and W. J. Dally, “Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training,” 2017
2017
Earlier work this paper cites.
J. Salamon, D. MacConnell, M. Cartwright, P. Li, and J. P. Bello, “Scaper: A library for soundscape synthesis and augmentation,” in Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) , 2017
2017
Earlier work this paper cites.
J. Salamon and J. P. Bello, “Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification,” IEEE Signal Processing Letters , vol. 24, no. 3, pp. 279–283, 3 2017
2017
Earlier work this paper cites.

Similar

A. Bhowmick, J. Duchi, J. Freudiger, G. Kapoor, and R. Rogers, “Protection against reconstruction and its applications in private federated learning,” arXiv preprint arXiv:1812.00984 , 2018
Original
2018
Cited alongside, same era.
J. Bernstein, Y.-X. Wang, K. Azizzadenesheli, and A. Anandkumar, “signSGD: Compressed optimisation for non-convex problems,” 2018
2018
Cited alongside, same era.
Y. Wu, “Group Normalization,” ECCV , 2018. [Online]. Available: https://research.fb.com/publications/group-normalization/
2018
Cited alongside, same era.
E. Cakir and T. Virtanen, “End-to-End Polyphonic Sound Event Detection Using Convolutional Recurrent Neural Networks with Learned Time-Frequency Representation Input,” Proceedings of the International Joint Conference on Neural Networks , vol. 2018-July, 2018
2018
Cited alongside, same era.
J.-R. Gloaguen, M. Lagrange, A. Can, and J.-F. Petiot, “Isolated urban sound database,” Apr. 2018. [Online]. Available: https://doi.org/10.5281/zenodo.1213793
2018
Cited alongside, same era.
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, and et al., “Advances and Open Problems in Federated Learning,” 2019
2019
Cited alongside, same era.
D. Leroy, A. Coucke, T. Lavril, T. Gisselbrecht, and J. Dureau, “Federated Learning for Keyword Spotting,” in Proceedings of ICASSP 2019 , 2019
2019
Cited alongside, same era.
Apple. (2019) Designing for privacy (video and slide deck). [Online]. Available: https://developer.apple.com/videos/play/wwdc2019/708
2019
Cited alongside, same era.
F. Sattler, S. Wiedemann, K.-R. Muller, and W. Samek, “Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data,” IEEE Transactions on Neural Networks and Learning Systems , 2019
2019
Cited alongside, same era.
F. Sattler, K.-R. Müller, and W. Samek, “Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints,” 2019
2019
Cited alongside, same era.

Then

Y. Hou, Q. Kong, S. Li, and M. D. Plumbley, “Sound Event Detection with Sequentially Labelled Data Based on Connectionist Temporal Classification and Unsupervised Clustering,” in Proceedings of ICASSP 2019 , 2019
2019
Later among the works it cites.
K. Koutini, H. Eghbal-zadeh, M. Dorfer, and G. Widmer, “The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification,” in Proceedings of EUSIPCO 2019 , 2019
2019
Later among the works it cites.
G. Chen, P. Chen, Y. Shi, C.-Y. Hsieh, B. Liao, and S. Zhang, “Rethinking the usage of batch normalization and dropout in the training of deep neural networks,” arXiv preprint arXiv:1905.05928 , 2019
Original
2019
Later among the works it cites.
N. Turpault, R. Serizel, A. Parag Shah, and J. Salamon, “Sound event detection in domestic environments with weakly labeled data and soundscape synthesis,” in Workshop on Detection and Classification of Acoustic Scenes and Events , New York City, United States, Oct. 2019. [Online]. Available: https://hal.inria.fr/hal-02160855
2019
Later among the works it cites.
G. Chen, P. Chen, Y. Shi, C.-Y. Hsieh, B. Liao, and S. Zhang, “Rethinking the usage of batch normalization and dropout in the training of deep neural networks,” arXiv preprint arXiv:1905.05928 , 2019
Original
2019
Later among the works it cites.
K. Koutini, H. Eghbal-zadeh, M. Dorfer, and G. Widmer, “The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification,” in Proceedings of EUSIPCO 2019 , 2019
2019
Later among the works it cites.
R. Serizel, N. Turpault, A. Shah, and J. Salamon, “Sound event detection in synthetic domestic environments,” in Proceedings of ICASSP 2020 , Barcelona, Spain, May 2020
2020
Later among the works it cites.
F. Wiewel and B. Yang, “Entropy-based sample selection for online continual learning,” in Proceedings of the EUSIPCO 2020 , 2020
2020
Later among the works it cites.
M. D. McDonnell and W. Gao, “Acoustic Scene Classification Using Deep Residual Networks with Late Fusion of Separated High and Low Frequency Paths,” in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings , vol. 2020-May. IEEE, 2020, pp. 141–145
2020
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…