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A Deep Learning Approach for Urban Sound Classification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 24 |
| Year of Publication: 2023 |
| Authors: Sanjoy Barua, Tahmina Akter, Mahmud Abu Saleh Musa, Muhammad Anwarul Azim |
10.5120/ijca2023922991
|
Sanjoy Barua, Tahmina Akter, Mahmud Abu Saleh Musa, Muhammad Anwarul Azim . A Deep Learning Approach for Urban Sound Classification. International Journal of Computer Applications. 185, 24 ( Jul 2023), 8-14. DOI=10.5120/ijca2023922991
Abstract
Urban sound classification is the task of identifying the type of sound present in a given recording, such as car honks, pedestrian footsteps, or construction noise. Accurate classification of urban sounds is important for a variety of applications, including environmental monitoring, traffic management, and public safety. To address this problem, we experiment with five different deep learning models: ANN, CNN, RNN, LSTM plus GRU combined model, and Bi-LSTM plus Bi-GRU model. These models are trained and evaluated on the Urban Sound 8K dataset, which consists of 8,000 urban sound recordings from 10 different classes. Our results show that the ANN model achieved the highest accuracy, reaching 95% on the test set. Overall, our results demonstrate the effectiveness of deep learning for urban sound classification and suggest that the ANN model is the most suitable for this task. This work has the potential to impact a variety of fields that rely on the accurate identification of urban sounds.
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