Affiliations: [a] School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India
| [b] Petroleum-Gas University of Ploiesti, Ploiesti, Romania
Correspondence:
[*]
Corresponding author. Sanjiban Sekhar Roy, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India. E-mail: sanjibansroy@ieee.org.
Abstract: In the recent time, enviromental sound classification has received much popularity. This area of research comes under domain of non-speech audio classification. In this work, we have proposed a dilated Convolutional Neural Network approch to classify urban sound. We have carried out feature extraction, data augmentation techniques to carry out our experimental strategy smoothly. We also found out the activation maps of each layers of dilated convolution neural network. An increamental dilation rate has exploited Overall we achieved 84.16% of accuracy from the proposed dilated convolutional method. The gradual increaments of dilation rate has exploited the worse effect of grindding and has lowered down the computational cost. Also, overall classification performance, precision, recall,overall truth and kappa value have been obtained from our proposed method. We have considered 10 fold cross validation for the implementation of the dilated CNN model.
