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Smart city article details

Title	Object Recognition Through Content Based Feature Extraction And Classification Of Sounds In Iot Environment
ID_Doc	39611
Authors	Sethi S.; Rath M.; Kuanar S.K.; Sahoo R.K.
Year	2024
Published	Communications in Computer and Information Science, 1892 CCIS
DOI	http://dx.doi.org/10.1007/978-3-031-56998-2_6
Abstract	The content-based order of urban sound classes is an important aspect of several emerging techniques and applications as a result, the research issue has gotten a lot of attention recently. The goal of this work is to develop an effective Machine Learning(ML) based approach for urban sound categorization in cities for intelligent object detection and recognition through sound for environmental study. Audio samples from urban space has been classified through various ML algorithms based on Mel spectrogram technique. Where, mel spectrogram techniques is a frequency based approach for extraction of features from audio samples for classification. The Urban Sound dataset, which contains a total of 8732 number od sound samples belongs to ten different classes, has been used in this work. This may be used for smart object detection through sound coming from objects in different applications, such as Traffic Management, Agriculture, Smart city, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Author Keywords	Audio Classification; environmental study; Machine Learning(ML); Mel spectrogram; smart object detection; Urban Sound categorization

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