Robust audio surveillance using spectrogram image texture feature

Roneel V. Sharan, Tom J. Moir

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

6 Citations (Scopus)

Abstract

A sound signal produces a unique texture which can be visualized using a spectrogram image and analyzed for automatic sound recognition. In this paper, we explore the use of a well-known image texture analysis technique called the gray-level co-occurrence matrix (GLCM) for sound recognition in an audio surveillance application. The GLCM captures the distribution of co-occurring values at a given offset. Unlike most other similar research which derive features from the GLCM, we use the matrix values itself to form the feature vector with analysis carried out in subbands. When compared to a baseline feature from related work, the proposed spectrogram image texture feature (SITF) gives marginally lower results under clean and high signal-to-noise ratio (SNR) conditions but significantly better results are achieved at low SNR, where the baseline feature was seen to be less effective.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1956-1960
Number of pages5
Volume2015-August
ISBN (Electronic)9781467369978
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: 19 Apr 201424 Apr 2014

Conference

Conference40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
CountryAustralia
CityBrisbane
Period19/04/1424/04/14

Keywords

  • Audio surveillance
  • gray-level cooccurrence matrix
  • sound recognition
  • spectrogram image texture feature
  • support vector machine

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  • Cite this

    Sharan, R. V., & Moir, T. J. (2015). Robust audio surveillance using spectrogram image texture feature. In 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings (Vol. 2015-August, pp. 1956-1960). [7178312] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICASSP.2015.7178312