Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16480
Title: Low-power SVM classifiers for sound event classification on mobile devices
Authors: Mak, MW 
Kung, SY
Keywords: Low-power SVM
Audio surveillance
Kernel-energy tradeoff
Smartphones
Sound event classification
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 25-30 March 2012, Kyoto, p. 1985-1988 How to cite?
Abstract: With the high processing power of today's smartphones, it becomes possible to turn a smartphone into a personal audio surveillance and monitoring system. Ideally, such a system should be able to detect and classify a variety of sound events 24 hours a day and trigger an emergence phone call or message once a specified sound event (e.g., screaming) occurs. To prolong battery life, it is important to trade off the detection accuracy against power consumption. This paper investigates the power consumption of different stages of a sound-event classification system, including segmentation, feature extraction, and SVM scoring. The performance and power consumption of various acoustic features and SVM kernels are compared. This paper advocates the notion of intrinsic complexity through which the scoring function of polynomial SVMs can be written in a matrix-vector-multiplication form so that the resulting complexity becomes independent of the number of support vectors. Results show that this intrinsic complexity can reduce the CPU utilization of polynomial SVMs by 28 times without reducing classification accuracy.
URI: http://hdl.handle.net/10397/16480
ISBN: 978-1-4673-0045-2
978-1-4673-0044-5 (E-ISBN)
ISSN: 1520-6149
DOI: 10.1109/ICASSP.2012.6288296
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

4
Last Week
0
Last month
Citations as of Feb 25, 2017

Page view(s)

40
Last Week
2
Last month
Checked on Aug 21, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.