Enhanced LBP texture features from time frequency representations for acoustic scene classification

Shamsiah Abidin, Roberto Togneri, Ferdous Sohel

Research output: Chapter in Book/Conference paperConference paperpeer-review

12 Citations (Scopus)

Abstract

This paper introduces the use of local binary patterns (LBP) extracted from a time-frequency representation (TFR) for acoustic scene classification. As LBP provides a description of the global TFR texture we propose a novel zoning mechanism that provides a simple solution to extract spectrally relevant local features which better characterize the audio TFRs. To further improve the classification performance, we perform feature and score level fusion of the proposed LBP (with zoning) with histogram of gradients (HOG) of the TFR images. Our technique demonstrates an improved performance by achieving a classification accuracy of 95.2% using a fusion of time-frequency derived features.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings
EditorsMagdy A. Bayoumi
Place of PublicationNew Orleans
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages626-630
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 19 Jun 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Conference

Conference2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Abbreviated titleICASSP 2017
Country/TerritoryUnited States
CityNew Orleans
Period5/03/179/03/17

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