Local binary pattern with random forest for acoustic scene classification

S. Abidin; X. Xia; R. Togneri; F. Sohel

doi:10.1109/ICME.2018.8486578

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Conference paper

Local binary pattern with random forest for acoustic scene classification

S. Abidin, X. Xia, R. Togneri and F. Sohel

2018 IEEE International Conference on Multimedia and Expo (ICME)

IEEE International Conference on Multimedia and Expo (ICME) 2018 (San Diego, CA, 23/07/2018–27/07/2018)

2018

DOI: https://doi.org/10.1109/ICME.2018.8486578

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Abstract

This paper presents an approach for acoustic scene classification using the local binary pattern (LBP) and random forest (RF). The audio signal is converted to a Constant-Q transform (CQT) representation and LBP is used to extract the features from this time-frequency representation. The CQT representations are divided into a number of sub-bands to obtain more localized features relevant to the spectral information. We then use random forest to select the most important features for each band of extracted LBP features. For further performance enhancement, we use feature level fusion of LBP and HOG features. The proposed system has achieved an accuracy of 85% on the DCASE 2016 dataset.

Details

Title: Local binary pattern with random forest for acoustic scene classification
Authors/Creators: S. Abidin (Author/Creator) - The University of Western Australia
X. Xia (Author/Creator) - The University of Western Australia
R. Togneri (Author/Creator) - The University of Western Australia
F. Sohel (Author/Creator) - Murdoch University
Publication Details: 2018 IEEE International Conference on Multimedia and Expo (ICME)
Conference: IEEE International Conference on Multimedia and Expo (ICME) 2018 (San Diego, CA, 23/07/2018–27/07/2018)
Identifiers: 991005542936007891
Murdoch Affiliation: School of Engineering and Information Technology
Language: English
Resource Type: Conference paper

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