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Abnormal Acoustic Event Detection Based on Orthogonal Matching Pursuit in Security Surveillance System

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Abstract

This paper presents a feature extraction approach for surveillance system aimed at achieving the automatic detection and recognition of public security events. The proposed approach first generates a Gabor dictionary based on the human auditory critical frequency bands, and then uses the orthogonal matching pursuit (OMP) algorithm to sparse abnormal audio signal. We select the optimal several important atoms from the Gabor dictionary and extract the scale, frequency, and translation parameters of the atoms to form the OMP feature. The performance of OMP feature is compared with traditional acoustic features and their joint features, using support vector machine (SVM) and random forest (RF) classifiers. Experiments have been performed to evaluate the effectiveness of the OMP feature for supplementing traditional acoustic features. The results show the superior performance classifier for abnormal acoustic event detection (AAED) is RF. Furthermore, the introduction of the combined features addresses the problems of low recognition accuracy and poor robustness for the surveillance system in practical applications.

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Acknowledgements

This work was partially supported by the NSFC Grants 51675425, Natural Science Basic Research Plan in Shaanxi Province of China Grant Nos. 2018SF-365 and 2020ZDLGY06-09, the Dongguan Social Science and Technology Development (key) Project 20185071021600.

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Authors and Affiliations

  1. School of Telecommunications Engineering, Xidian University, Xi’an, 710071, Shaanxi, People’s Republic of China

    Pengnan Zhang & Juan Wei

  2. School of Mechanical and Electrical Engineering, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, People’s Republic of China

    Zhe Liu & Fangli Ning

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  1. Pengnan Zhang
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  2. Juan Wei
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  3. Zhe Liu
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  4. Fangli Ning
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Correspondence to Juan Wei.

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Zhang, P., Wei, J., Liu, Z. et al. Abnormal Acoustic Event Detection Based on Orthogonal Matching Pursuit in Security Surveillance System. Wireless Pers Commun 114, 1009–1024 (2020). https://doi.org/10.1007/s11277-020-07405-z

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  • DOI: https://doi.org/10.1007/s11277-020-07405-z

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