Comparison of multiclass SVM classification techniques in an audio surveillance application under mismatched conditions

Roneel V. Sharan, Tom J. Moir

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

12 Citations (Scopus)

Abstract

In this paper, we compare the performance of classification techniques for multiclass support vector machines in an unstructured environment. In particular, we consider the following methods: one-against-all, one-against-one, decision directed acyclic graph, and adaptive directed acyclic graph. The performance is compared in terms of classification accuracy, training time, and evaluation time. An audio surveillance application is looked at under different noise conditions and varying signal-to-noise ratio with mel-frequency cepstral coefficients and other commonly used time and frequency domain features. The results show that while there isn't much difference in the classification accuracy using the four approaches under clean and low noise conditions, the oneagainst-all method was found to give relatively better classification accuracy in high noise conditions when trained with clean samples only. However, the results were much more even with multi-conditional training. Also, the training time for the one-against-all approach was found to increase significantly as the training data increased fourfold while the one-against-one approach showed a significantly higher evaluation time.

Original languageEnglish
Title of host publication2014 19th International Conference on Digital Signal Processing, DSP 2014
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages83-88
Number of pages6
ISBN (Electronic)9781479946129
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event2014 19th International Conference on Digital Signal Processing, DSP 2014 - Hong Kong, Hong Kong
Duration: 20 Aug 201423 Aug 2014

Conference

Conference2014 19th International Conference on Digital Signal Processing, DSP 2014
CountryHong Kong
CityHong Kong
Period20/08/1423/08/14

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Keywords

  • Audio surveillance
  • Signal-to-noise ratio
  • Sound recognition
  • Support vector machines

Cite this

Sharan, R. V., & Moir, T. J. (2014). Comparison of multiclass SVM classification techniques in an audio surveillance application under mismatched conditions. In 2014 19th International Conference on Digital Signal Processing, DSP 2014 (pp. 83-88). [6900805] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICDSP.2014.6900805