Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17914
Title: Robust scream sound detection via sound event partitioning
Authors: Lei, B
Mak, MW 
Keywords: Feature normalization
Regularized PCA-whitening
Scream sound detection
Sound event partitioning
Issue Date: 2015
Publisher: Kluwer Academic Publishers
Source: Multimedia tools and applications, 2015 How to cite?
Journal: Multimedia Tools and Applications 
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.
URI: http://hdl.handle.net/10397/17914
ISSN: 1380-7501
DOI: 10.1007/s11042-015-2555-z
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