An approach for monitoring sand mining based on sound feature. Issue 1 (April 2020)

• Record Type: Journal Article
• Authors: Zhou, Qingmei; He, Xiping
• Is Part Of: IOP conference series. Volume 806:Issue 1(2020)
• Publication Date: 2020-04
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/806/1/012052
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: In order to strengthen the management of sand excavation in river courses and prevent the occurrence of illegal sand mining activities, this paper proposes an approach for monitoring sand mining based on sound. Firstly, Mel Frequency Cepstral Coefficients (MFCCs) abstractor and Autoencoder are combined to extract features of every frame of a sound sequence, and then each frame of the sound sequence is classified by a specific classifier. Finally a voting strategy is used among the frames to determine the final category of the sound sequence. Experiments show that whether the classifier is SVM, KNN, or BP neural network, the result of combined features is better than the result of features extracted by the MFCC abstractor. Therefore, it is feasible to use the artificial intelligence method based on sound features extracted through MFCC abstractor and Autoencoder to monitor sand mining.