Deep convolutional neural network for environmental sound classification via dilation

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 extracti...

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Bibliographic Details
Published inJournal of intelligent & fuzzy systems Vol. 43; no. 2; pp. 1827 - 1833
Main Authors Roy, Sanjiban Sekhar, Mihalache, Sanda Florentina, Pricop, Emil, Rodrigues, Nishant
Format Journal Article
LanguageEnglish
Published Amsterdam IOS Press BV 09.06.2022
Subjects
Artificial neural networks
Background noise
Classification
Dilation
Feature extraction
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Summary: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.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-219283