Implementation of a deep convolutional network for environmental sound classification task using ESC10 and ESC50 datasets.

The main dependencies of the project are:

• Python 3.7.9
• Tensorflow-gpu 2.3.0
• cudatoolkit 10.1.243
• cudnn 7.6.5
• Librosa 0.8.0

We firstly tried different architecture from the references with slight changes. In the end the final model, Multi Feature Network - MFNet, was inspired mainly by [1], inception and residual blocks. The final performance is very similar to [1] being basically a derivation.

• Aniello Xie
• Carlo Facchin

[1] Karol J Piczak. Environmental sound classification with convolutional neural networks. In 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), pages 1–6. IEEE, 2015. [2] Shaobo Li, Yong Yao, Jie Hu, Guokai Liu, Xuemei Yao, and Jianjun Hu. An ensemble stacked convolutional neural network model for environmental event sound recognition. Applied Sciences, 8(7):1152, 2018.

[3] Andrey Guzhov, Federico Raue, Jorn Hees, and An- ¨ dreas Dengel. Esresnet: Environmental sound classification based on visual domain models. arXiv preprint arXiv:2004.07301, 2020.

[4] Justin Salamon and Juan Pablo Bello. Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Processing Letters, 24(3):279–283, 2017.

[5] Rene Schuster, Oliver Wasenmuller, Christian Unger, and ´ Didier Stricker. Sdc-stacked dilated convolution: A unified descriptor network for dense matching tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2556–2565, 2019.

[6] Yuji Tokozume and Tatsuya Harada. Learning environmental sounds with end-to-end convolutional neural network. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2721–2725. IEEE, 2017.

[7] Zhichao Zhang, Shugong Xu, Shan Cao, and Shunqing Zhang. Deep convolutional neural network with mixup for environmental sound classification. In Chinese conference on pattern recognition and computer vision (prcv), pages 356–367. Springer, 2018.