Smart City Gnosys

Smart city article details

Title	Investigation Of The Effectiveness Of Time-Frequency Domain Images And Acoustic Features In Urban Sound Classification
ID_Doc	33493
Authors	Özseven T.
Year	2023
Published	Applied Acoustics, 211
DOI	http://dx.doi.org/10.1016/j.apacoust.2023.109564
Abstract	Rapid urbanization and population growth worldwide seriously challenge building livable and sustainable cities. This increase causes the increase and diversification of urban sounds. They were transforming these sounds into information instead of just being heard, as noise plays an important role in the concept of smart cities. For this purpose, two basic methods are used to classify urban sounds. In the first of these, the sounds are processed by signal processing methods, and handcrafted features are obtained. In the other method, sounds are represented visually and classified with deep learning models. This study investigated the effect of the individual and hybrid use of features used in both approaches on the classification of urban sounds. In addition, a CNN model was created to classify hybrid features. The results obtained showed that both approaches produced successful results in classification. Among the visual representation methods, mel-spectrogram, scalogram, and spectrogram images achieved the highest classification success. Using mel-spectrogram and acoustic features and the SVM classifier positively affected accuracy. Experiments were performed on the ESC-10 and UrbanSound8k datasets. The highest accuracy for the ESC-10 was 98.33% when using the scalogram and acoustic features with the AVCNN model. The highest accuracy for UrbanSound8k was obtained as 97.70% by classifying the mel-spectrogram and acoustic features obtained from the AVCNN model with the SVM classifier. © 2023 Elsevier Ltd
Author Keywords	Acoustic analysis; Audio-visual feature set; Cepstral features; Deep learning; Environmental sound classification; Smart city; Sound event recognition; Urban sound recognition

Similar Articles

Id	Similarity	Authors	Title	Published
52318	0.92	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)
14305	0.914	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)
60186	0.906	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)
7700	0.899	Zhang D.; Zhong Z.; Xia Y.; Wang Z.; Xiong W.	An Automatic Classification System For Environmental Sound In Smart Cities	Sensors, 23, 15 (2023)
24259	0.896	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)
14284	0.896	Reddy B.S.; Chowdary D.M.; Srinivas R.; Rahmani M.O.	Classification Of Environmental And Urban Sounds Using Deep Learning Techniques	4th IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2025 (2025)
60187	0.895	Lakshmi R.; Chaitra N.C.; Thejaswini R.; Swapna H.; Parameshachari B.D.; Kumar S.D.S.; Puttegowda K.	Urban Sound Classification With Convolutional Neural Network	2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024 (2024)
59656	0.893	Hidayat A.; Njoo D.B.P.; Adrian G.D.; Setyoko D.E.; Wijanarko B.D.	Unlocking Soundscapes: Harnessing Machine Learning For Sound Classification	Proceeding of 2024 9th International Conference on Information Technology and Digital Applications, ICITDA 2024 (2024)
52335	0.889	Chatterjee R.; Bishwas P.; Chakrabarty S.; Bandyopadhyay T.	South Asian Sounds: Audio Classification	Proceedings - 2024 4th International Conference on Computer, Communication, Control and Information Technology, C3IT 2024 (2024)
14555	0.887	Seker H.; Inik O.	Cnnsound: Convolutional Neural Networks For The Classification Of Environmental Sounds	ACM International Conference Proceeding Series (2020)