Smart City Gnosys

Smart city article details

Title	Iot-Based Urban Noise Identification Using Machine Learning: Performance Of Svm, Knn, Bagging, And Random Forest
ID_Doc	34029
Authors	Alsouda Y.; Pllana S.; Kurti A.
Year	2019
Published	ACM International Conference Proceeding Series, Part F148162
DOI	http://dx.doi.org/10.1145/3312614.3312631
Abstract	Noise is any undesired environmental sound. A sound at the same dB level may be perceived as annoying noise or as pleasant music. Therefore, it is necessary to go beyond the state-of-the-art approaches that measure only the dB level and also identify the type of noise. In this paper, we present a machine learning based method for urban noise identification using an inexpensive IoT unit. We use Mel-frequency cepstral coefficients for audio feature extraction and supervised classification algorithms (that is, support vector machine, k-nearest neighbors, bootstrap aggregation, and random forest) for noise classification. We evaluate our approach experimentally with a data-set of about 3000 sound samples grouped in eight sound classes (such as car horn, jackhammer, or street music). We explore the parameter space of the four algorithms to estimate the optimal parameter values for classification of sound samples in the data-set under study.We achieve a noise classification accuracy in the range 88% - 94%. © 2019 Copyright held by the owner/author(s).
Author Keywords	Bootstrap aggregation (Bagging); Internet of things (IoT); K-nearest neighbors (KNN); Mel-frequency cepstral coefficients (MFCC); Random forest; Smart cities; Support vector machine (SVM); Urban noise

Similar Articles

Id	Similarity	Authors	Title	Published
2468	0.939	Ali Y.H.; Rashid R.A.; Hamid S.Z.A.	A Machine Learning For Environmental Noise Classification In Smart Cities	Indonesian Journal of Electrical Engineering and Computer Science, 25, 3 (2022)
33514	0.915	Albaji A.O.; Rashid R.B.A.; Abdul Hamid S.Z.	Investigation On Machine Learning Approaches For Environmental Noise Classifications	Journal of Electrical and Computer Engineering, 2023 (2023)
21820	0.894	Baucas M.J.; Spachos P.	Edge-Based Data Sensing And Processing Platform For Urban Noise Classification	IEEE Sensors Letters, 8, 5 (2024)
39611	0.893	Sethi S.; Rath M.; Kuanar S.K.; Sahoo R.K.	Object Recognition Through Content Based Feature Extraction And Classification Of Sounds In Iot Environment	Communications in Computer and Information Science, 1892 CCIS (2024)
52316	0.891	Bello J.P.; Mydlarz C.; Salamon J.	Sound Analysis In Smart Cities	Computational Analysis of Sound Scenes and Events (2017)
33639	0.882	Machap K.; Narani S.R.	Iot Audio Sensor Networks And Decision Trees For Enhanced Rain Sound Classification	International Journal Of Advances In Signal And Image Sciences, 10, 1 (2024)
24672	0.875	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)
52318	0.874	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)
33493	0.87	Özseven T.	Investigation Of The Effectiveness Of Time-Frequency Domain Images And Acoustic Features In Urban Sound Classification	Applied Acoustics, 211 (2023)
7700	0.869	Zhang D.; Zhong Z.; Xia Y.; Wang Z.; Xiong W.	An Automatic Classification System For Environmental Sound In Smart Cities	Sensors, 23, 15 (2023)