Smart City Gnosys

Smart city article details

Title	Unlocking Soundscapes: Harnessing Machine Learning For Sound Classification
ID_Doc	59656
Authors	Hidayat A.; Njoo D.B.P.; Adrian G.D.; Setyoko D.E.; Wijanarko B.D.
Year	2024
Published	Proceeding of 2024 9th International Conference on Information Technology and Digital Applications, ICITDA 2024
DOI	http://dx.doi.org/10.1109/ICITDA64560.2024.10809414
Abstract	The concept of Smart Cities has been implemented in several major areas to improve the lives of citizens. One of the technologies used is sound classification, which monitors and identifies levels of urban noise. Convolutional Neural Networks (CNNs) have proven to be an effective method for sound classification because they can recognize complex patterns in audio data. Various architectures of Convolutional Neural Networks, such as ResNet, DenseNet, and others, exist for this purpose. This research aims to identify the CNN architecture that performs best in urban sound classification. The methodology involves training and testing existing CNN architecture and evaluating them based on several parameters, including the number of parameters, memory usage, convergence speed, and training time. Experimental results show that the performance of the ResNet family tends to degrade when the network depth increases. The best performance was achieved by the Resnet-18 architecture, with accuracy, precision, recall and F1-Score values of 91.5%, 92.2%, 91.2%, 91.6%. On the other hand, the use of complex features from various scales causes the DenseNet and GoogleNet architectures to be unableto achieve the best performance. © 2024 IEEE.
Author Keywords	CNN Architectures; Convolutional Neural Networks (CNN); Noise Pollution; Smart City; Urban Sound Classification

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