| Issue |
E3S Web of Conf.
Volume 405, 2023
2023 International Conference on Sustainable Technologies in Civil and Environmental Engineering (ICSTCE 2023)
|
|
|---|---|---|
| Article Number | 02017 | |
| Number of page(s) | 9 | |
| Section | Renewable Energy & Electrical Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202340502017 | |
| Published online | 26 July 2023 | |
Ambient Sound Recognition using Convolutional Neural Networks
1 Department of Electronics & Communication Engineering, Chandigarh University, Punjab, India
2 Department of Aerospace Engineering, Chandigarh University, Punjab, India
* Corresponding author: chandelgarima5@gmail.com
Due to its many uses in areas including voice recognition, music analysis, and security systems, sound recognition has attracted a lot of attention. Convolutional neural networks (CNNs) have become a potent tool for sound recognition, producing cutting-edge outcomes in a variety of challenges. In this study, we will look at the architecture of CNNs, several training methods used to enhance their performance, and accuracy testing. The performance of the proposed sound recognition technique has been tested using 1000 audio files from the UrbanSounds8K dataset. The accuracy results obtained by using a CNN and Support Vector Machine (SVM) models were 95.6% and 93% respectively. These results portray the efficiency of using an advanced CNN architecture with five convolution layers and a versatile dataset like Urbansoundsd8K.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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