CnnSound: Convolutional Neural Networks for the Classification of Environmental Sounds
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inik, ozkan and SEKER, Huseyin (2020) CnnSound: Convolutional Neural Networks for the Classification of Environmental Sounds. In: ICAAI 2020: Proceedings of the 2020 4th International Conference on Advances in Artificial Intelligence. ACM. ISBN 978-1-4503-8784-2 (In Press)
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Ozkan Huseyin EAIT 2020 IJIET_template Final 10102020.pdf - AUTHOR'S ACCEPTED Version (default) Available under License All Rights Reserved. Download (690kB) | Preview |
Abstract or description
The classification of environmental sounds (ESC) has been increasingly studied in recent years. The main reason is that environmental sounds are part of our daily life, and associating them with our environment that we live in is important in several aspects as ESC is used in areas such as managing smart cities, determining location from environmental sounds, surveillance systems, machine hearing, environment monitoring. The ESC is however more difficult than other sounds because there are too many parameters that generate background noise in the ESC, which makes the sound more difficult to model and classify. The main aim of this study is therefore to develop more robust convolution neural networks architecture (CNN). For this purpose, 150 different CNN-based models were designed by changing the number of layers and values of their tuning parameters used in the layers. In order to test the accuracy of the models, the Urbansound8k environmental sound database was used. The sounds in this data set were first converted into an image format of 32x32x3. The proposed CNN model has yielded an accuracy of as much as 82.5% being higher than its classical counterpart. As there was not that much fine-tuning, the obtained accuracy has been found to be better and satisfactory compared to other studies on the Urbansound8k when both accuracy and computational complexity are considered. The results also suggest further improvement possible due to low complexity of the proposed CNN architecture and its applicability in real-world settings.
| Item Type: | Book Chapter, Section or Conference Proceeding |
|---|---|
| Additional Information: | Presented at The 4th International Conference on Advances in Artificial Intelligence 13-15 November 2020 |
| Faculty: | School of Computing and Digital Technologies > Computing |
| Event Title: | The 4th International Conference on Advances in Artificial Intelligence |
| Event Location: | Online |
| Event Dates: | 13-15 November 2020 |
| Depositing User: | Huseyin SEKER |
| Date Deposited: | 07 Dec 2020 09:19 |
| Last Modified: | 24 Feb 2023 14:00 |
| Related URLs: | |
| URI: | https://eprints.staffs.ac.uk/id/eprint/6640 |
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