Cochleagram image feature for improved robustness in sound recognition

Roneel V. Sharan, Tom J. Moir

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

12 Citations (Scopus)

Abstract

In this paper, we use the cochleagram image of sound signals for time-frequency analysis and feature extraction, instead of the conventional spectrogram image, in an audio surveillance application. The signal is firstly passed through a gammatone filter which models the auditory filters in the human cochlea. The filtered signal is then divided into small windows and the energy in each window is added and normalized which gives the intensity values of the cochleagram image. We then divide the cochleagram image into blocks and extract central moments as features. Using two feature vector representation methods, the results show significant improvement in overall classification accuracy when compared to results from literature employing similar feature extraction and representation techniques but using spectrogram images. The most improved results were at low signal-to-noise ratios.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Digital Signal Processing, DSP 2015
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages441-444
Number of pages4
Volume2015-September
ISBN (Electronic)9781479980581
DOIs
Publication statusPublished - 9 Sep 2015
Externally publishedYes
EventIEEE International Conference on Digital Signal Processing, DSP 2015 - Singapore, Singapore
Duration: 21 Jul 201524 Jul 2015

Conference

ConferenceIEEE International Conference on Digital Signal Processing, DSP 2015
CountrySingapore
CitySingapore
Period21/07/1524/07/15

Keywords

  • audio surveillance
  • central moments
  • cochleagram
  • sound recognition
  • support vector machine

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