Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55534
Title: Sound-event partitioning and feature normalization for robust sound-event detection
Authors: Lei, B
Mak, MW 
Keywords: Feature normalization
PCA whitening and regularization
Scream sound detection
Sound event partitioning
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 2014 19th International Conference on Digital Signal Processing : Hong Kong, 20-23 August 2014, 6900692, p. 389-394 How to cite?
Abstract: The ubiquitous of smartphones has opened up the possibility of mobile acoustic surveillance. However, the continuous operation of surveillance systems calls for efficient algorithms to conserve battery consumption. This paper proposes a power-efficient sound-event detector that exploits the redundancy in the sound frames. This is achieved by a soundevent partitioning (SEP) scheme where the acoustic vectors within a sound event are partitioned into a number of chunks, and the means and standard deviations of the acoustic features in the chucks are concatenated for classification by a support vector machine (SVM). Regularized PCA-whitening and L2 normalization are applied to the acoustic vectors to make them more amenable for the SVM. Experimental results based on 1000 sound events show that the proposed scheme is effective even if there are severe mismatches between the training and test conditions.
URI: http://hdl.handle.net/10397/55534
ISBN: 9781479946129
DOI: 10.1109/ICDSP.2014.6900692
Appears in Collections:Conference Paper

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