Smart City Gnosys

Smart city article details

Title	Towards Robust Environmental Sound Classification: A Deep Learning Approach Leveraging Time-Frequency Representations
ID_Doc	58279
Authors	Vijay M.; Ruthwik Saran K.; Reddy K.R.; Aditya Ram K.; Babu J.Y.
Year	2024
Published	2nd International Conference on Emerging Research in Computational Science, ICERCS 2024
DOI	http://dx.doi.org/10.1109/ICERCS63125.2024.10895235
Abstract	Intelligent sound classification is essential for a number of applications, from autonomous systems to smart city surveillance Recent advancements in The accuracy and efficiency of sound classification tasks can be significantly increased with machine learning. This paper presents a novel approach for environment sound classification that integrates the powerful audio processing capabilities of Librosa with the deep learning frameworks of PyTorch. Evaluate our method on the large dataset of ambient noises, and the findings demonstrate a significant improvement in classification accuracy compared to baseline approaches and how various feature extraction methods and network settings affect performance, as well as the difficulties that arise while training and evaluating models. The outcomes highlight how well Librosa's extensive audio feature extraction and PyTorch's adaptable deep learning capabilities work together to push the boundaries of environmental sound classification. © 2024 IEEE.
Author Keywords	and Machine learning; Audio processing; deep learning frameworks; feature extraction methods; Librosa; Pytorch; sound classification

Similar Articles

Id	Similarity	Authors	Title	Published
14555	0.908	Seker H.; Inik O.	Cnnsound: Convolutional Neural Networks For The Classification Of Environmental Sounds	ACM International Conference Proceeding Series (2020)
52318	0.894	Nogueira A.F.R.; Oliveira H.S.; Machado J.J.M.; Tavares J.M.R.S.	Sound Classification And Processing Of Urban Environments: A Systematic Literature Review	Sensors, 22, 22 (2022)
24672	0.893	Lamrini M.; Chkouri M.Y.; Touhafi A.	Evaluating The Performance Of Pre-Trained Convolutional Neural Network For Audio Classification On Embedded Systems For Anomaly Detection In Smart Cities	Sensors, 23, 13 (2023)
48795	0.89	Liu Z.; Yeh W.-C.	Simplified Swarm Optimisation For Cnn Hyperparameters: A Sound Classification Approach	International Journal of Web and Grid Services, 20, 1 (2024)
14305	0.89	Agarwal M.; Gill K.S.; Chattopadhyay S.; Singh M.	Classification Of Urban Sound Using Sequential Convolutional Neural Network (Cnn) Model And Its Visualisation	2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024 (2024)
24259	0.888	Das J.K.; Chakrabarty A.; Piran M.J.	Environmental Sound Classification Using Convolution Neural Networks With Different Integrated Loss Functions	Expert Systems, 39, 5 (2022)
58701	0.883	Goulão M.; Bandeira L.; Martins B.; L. Oliveira A.	Training Environmental Sound Classification Models For Real-World Deployment In Edge Devices	Discover Applied Sciences, 6, 4 (2024)
7700	0.879	Zhang D.; Zhong Z.; Xia Y.; Wang Z.; Xiong W.	An Automatic Classification System For Environmental Sound In Smart Cities	Sensors, 23, 15 (2023)
60186	0.879	Agarwal M.; Gill K.S.; Aggarwal P.; Rawat R.S.; Sunil G.	Urban Sound Classification Using Vgg19 Convolutional Neural Network (Cnn) Model And Its Visualisation	4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024 (2024)
14548	0.874	İnik Ö.	Cnn Hyper-Parameter Optimization For Environmental Sound Classification	Applied Acoustics, 202 (2023)