@inproceedings{fa9bd39980624ff2b373c9e6ef1e8b4e,
title = "Research on Acoustic Anomaly Detection in Public Scene Based on Multi-dimensional Feature Space",
abstract = "Aiming at the problem of detection and recognition of abnormal sound events in public scenes, this paper proposes an algorithm based on machine hearing algorithm to automatically complete the detection and classification of abnormal sound activities. Through real-time monitoring of the scene, template matching is carried out with the list of abnormal events to realize the judgment of abnormal sound activities in public scenes. The feature mapping of multi-dimensional vector space is completed for the speech segments of potential acoustic activity. The feature vector includes not only the own features, but also the features related to the event list template. The SVM algorithm based on Gaussian radial basis function is used to train and test the performance on the self-organized dataset. The results show that the algorithm has a good performance in detecting the accuracy of classification.",
keywords = "Acoustic activity detection, Feature extraction, Multi-dimensional vector space, SVM",
author = "Tongan Ji and Wenzhong Lou and Fei Zhao and Zilong Su",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Autonomous Unmanned Systems, ICAUS 2021 ; Conference date: 24-09-2021 Through 26-09-2021",
year = "2022",
doi = "10.1007/978-981-16-9492-9_121",
language = "English",
isbn = "9789811694912",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "1214--1224",
editor = "Meiping Wu and Yifeng Niu and Mancang Gu and Jin Cheng",
booktitle = "Proceedings of 2021 International Conference on Autonomous Unmanned Systems, ICAUS 2021",
address = "Germany",
}