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Robust scream sound detection via sound event partitioning

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Abstract

This paper proposes a robust scream-sound detection scheme for acoustic surveillance applications. To enhance the discriminability between scream and non-scream sounds, a sound-event partitioning (SEP) method that facilitates the extraction of multiple acoustic vectors from a single sound event is developed. Regularized principal component analysis (PCA) and normalization are applied to the acoustic vectors, which are then classified by support vector machines (SVMs). Experimental results based on 1000 sound events show that the proposed scheme is effective even if there are severe mismatches between the training and testing conditions. The experimental results also show that the proposed scheme can reduce the equal error rate (EER) by up to 60 % when compared to a classical approach that uses mel-frequency cepstral coefficients (MFCC) as features. Extensive analyses on different processing stages of the proposed sound detection scheme also suggest that sound partitioning and feature normalization play important roles in boosting the detection performance.

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Notes

  1. It is important to note that individual frames do not contain sufficient information for differentiating scream and non-scream sounds. In fact, individual frames of scream and non-scream sound are highly overlapped in the feature space, which will cause problems if they are directly used for training SVM classifiers.

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Acknowledgments

The work was supported partly by National Natural Science Foundation of China (No. 61402296), Motorola Solutions Foundation (ID: 7186445) and the Hong Kong Polytechnic University Grant No. G-YL78. The authors would like to thank Wing-Lung Leung for developing the sound recording system and part of the Android App.

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Authors and Affiliations

  1. Department of Biomedical Engineering, School of Medicine, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, and Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, 518060, Guangdong, China

    Baiying Lei

  2. Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Man-Wai Mak

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  1. Baiying Lei
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Correspondence to Baiying Lei.

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Lei, B., Mak, MW. Robust scream sound detection via sound event partitioning. Multimed Tools Appl 75, 6071–6089 (2016). https://doi.org/10.1007/s11042-015-2555-z

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