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Environmental Sound Classification (ESC) is a rapidly evolving field that recently demonstrated the advantages of application of visual domain techniques to the audio-related tasks.
1930
Earlier work this paper cites.
J. Volkmann, S. S. Stevens, and E. B. Newman, “A scale for the measurement of the psychological magnitude pitch,” The Journal of the Acoustical Society of America , vol. 8, no. 3, pp. 208–208, 1937. [Online]. Available: https://doi.org/10.1121/1.1901999
1937
Earlier work this paper cites.
J. Allen, “Short term spectral analysis, synthesis, and modification by discrete fourier transform,” IEEE Transactions on Acoustics, Speech, and Signal Processing , vol. 25, no. 3, pp. 235–238, June 1977
1977
Earlier work this paper cites.
Y. Nesterov, “A method of solving a convex programming problem with convergence rate o (1/kˆ 2) o (1/k2),” in Sov. Math. Dokl , vol. 27, no. 2, 1983
1983
Earlier work this paper cites.
B. T. Polyak and A. B. Juditsky, “Acceleration of stochastic approximation by averaging,” SIAM journal on control and optimization , vol. 30, no. 4, pp. 838–855, 1992
1992
Earlier work this paper cites.
M. Slaney et al. , “An efficient implementation of the patterson-holdsworth auditory filter bank,” Apple Computer, Perception Group, Tech. Rep , vol. 35, no. 8, 1993
1993
Earlier work this paper cites.
A. Teolis and J. J. Benedetto, Computational signal processing with wavelets . Springer, 1998, vol. 182
1998
Earlier work this paper cites.
A. V. Oppenheim, Discrete-time signal processing . Pearson Education India, 1999
1999
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in CVPR09 , 2009
2009
Earlier work this paper cites.
J. Salamon, C. Jacoby, and J. P. Bello, “A dataset and taxonomy for urban sound research,” in Proceedings of the 22nd ACM International Conference on Multimedia , ser. MM ’14. New York, NY, USA: Association for Computing Machinery, 2014, p. 1041–1044. [Online]. Available: https://doi.org/10.1145/2647868.2655045
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
K. J. Piczak, “Esc: Dataset for environmental sound classification,” in Proceedings of the 23rd ACM International Conference on Multimedia , ser. MM ’15. New York, NY, USA: Association for Computing Machinery, 2015, p. 1015–1018. [Online]. Available: https://doi.org/10.1145/2733373.2806390
2015
Cited alongside, same era.
K. J. Piczak, “Environmental sound classification with convolutional neural networks,” in 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP) , Sep. 2015, pp. 1–6
2015
Cited alongside, same era.
G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” in ICML deep learning workshop , vol. 2. Lille, 2015
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2016
2016
Cited alongside, same era.
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” 2017
2017
Later among the works it cites.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1251–1258
2017
Later among the works it cites.
B. Zhu, K. Xu, D. Wang, L. Zhang, B. Li, and Y. Peng, “Environmental sound classification based on multi-temporal resolution convolutional neural network combining with multi-level features,” in Pacific Rim Conference on Multimedia . Springer, 2018, pp. 528–537
2018
Later among the works it cites.
Z. Zhang, S. Xu, S. Cao, and S. Zhang, “Deep convolutional neural network with mixup for environmental sound classification,” in Chinese Conference on Pattern Recognition and Computer Vision (PRCV) . Springer, 2018, pp. 356–367
2018
Later among the works it cites.
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H. B. Sailor, D. M. Agrawal, and H. A. Patil, “Unsupervised filterbank learning using convolutional restricted boltzmann machine for environmental sound classification.” in INTERSPEECH , 2017, pp. 3107–3111
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 Proc. IEEE ICASSP 2017 , New Orleans, LA, 2017
2017
Cited alongside, same era.
Y. Tokozume and T. Harada, “Learning environmental sounds with end-to-end convolutional neural network,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , March 2017, pp. 2721–2725
2017
Cited alongside, same era.
2017
Cited alongside, same era.
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, 2017
2017
Cited alongside, same era.
R. N. Tak, D. M. Agrawal, and H. A. Patil, “Novel phase encoded mel filterbank energies for environmental sound classification,” in International Conference on Pattern Recognition and Machine Intelligence . Springer, 2017, pp. 317–325
2017
Cited alongside, same era.
S. Abdoli, P. Cardinal, and A. L. Koerich, “End-to-end environmental sound classification using a 1d convolutional neural network,” Expert Systems with Applications , vol. 136, pp. 252–263, 2019
2019
Later among the works it cites.
Z. Zhang, S. Xu, S. Zhang, T. Qiao, and S. Cao, “Learning attentive representations for environmental sound classification,” IEEE Access , vol. 7, pp. 130 327–130 339, 2019
2019
Later among the works it cites.
A. Arnault, B. Hanssens, and N. Riche, “Urban sound classification : striving towards a fair comparison,” 2020
2020
Later among the works it cites.
A. Kumar and V. Ithapu, “A sequential self teaching approach for improving generalization in sound event recognition,” in International Conference on Machine Learning . PMLR, 2020, pp. 5447–5457
2020
Later among the works it cites.
K. Palanisamy, D. Singhania, and A. Yao, “Rethinking cnn models for audio classification,” 2020
2020
Later among the works it cites.
A. Guzhov, F. Raue, J. Hees, and A. Dengel, “Esresnet: Environmental sound classification based on visual domain models,” in 25th International Conference on Pattern Recognition (ICPR) , January 2021, pp. 4933–4940
2021
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