Low-power SVM classifiers for sound event classification on mobile devices

Man Wai Mak, Sun-Yuan Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages1985-1988
Number of pages4
DOIs
StatePublished - Oct 23 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: Mar 25 2012Mar 30 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period3/25/123/30/12

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Low-power SVM
  • audio surveillance
  • kernel-energy tradeoff
  • smartphones
  • sound event classification

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  • Cite this

    Mak, M. W., & Kung, S-Y. (2012). Low-power SVM classifiers for sound event classification on mobile devices. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (pp. 1985-1988). [6288296] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2012.6288296