A New Deep CNN Model for Environmental Sound Classification - Demir, Fatih; Abdullah, Daban Abdulsalam; Sengur, Abdulkadir (Ieee-Inst Electrical Electronics Engineers Inc, 2020)
Cognitive prediction in the complicated and active environments is of great importance role in artificial learning. Classification accuracy of sound events has a robust relation with the feature extraction. In this paper, deep features are used in the environmental sound classification (ESC) problem. The deep features are extracted by using the fully connected layers of a newly developed Convolutional Neural Networks (CNN) model, which is trained in the end-to-end fashion with the spectrogram images. The feature vector is constituted with concatenating of the fully connected layers of the proposed CNN model. For testing the performance of the proposed method, the feature set is conveyed as input to the random subspaces K Nearest Neighbor (KNN) ensembles classifier. The experimental studies, which are carried out on the DCASE-2017 ASC and the UrbanSound8K datasets, show that the proposed CNN model achieves classification accuracies 96.23% and 86.70%, respectively.
Keywords: Environmental sound classification; spectrogram images; CNN model; deep features