Local Binary Pattern with Random Forest for Acoustic Scene Classification

Shamsiah Abidin, Xianjun Xia, Robert Togneri, Ferdous Sohel

Research output: Chapter in Book/Conference paperConference paper

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Multimedia and Expo, ICME 2018
PublisherIEEE, Institute of Electrical and Electronics Engineers
Volume2018-July
ISBN (Electronic)9781538617373
DOIs
Publication statusPublished - 8 Oct 2018
Event2018 IEEE International Conference on Multimedia and Expo, ICME 2018 - San Diego, United States
Duration: 23 Jul 201827 Jul 2018

Conference

Conference2018 IEEE International Conference on Multimedia and Expo, ICME 2018
CountryUnited States
CitySan Diego
Period23/07/1827/07/18

Fingerprint

Fusion reactions
Acoustics

Cite this

Abidin, S., Xia, X., Togneri, R., & Sohel, F. (2018). Local Binary Pattern with Random Forest for Acoustic Scene Classification. In 2018 IEEE International Conference on Multimedia and Expo, ICME 2018 (Vol. 2018-July). [8486578] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICME.2018.8486578
Abidin, Shamsiah ; Xia, Xianjun ; Togneri, Robert ; Sohel, Ferdous. / Local Binary Pattern with Random Forest for Acoustic Scene Classification. 2018 IEEE International Conference on Multimedia and Expo, ICME 2018. Vol. 2018-July IEEE, Institute of Electrical and Electronics Engineers, 2018.
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Abidin, S, Xia, X, Togneri, R & Sohel, F 2018, Local Binary Pattern with Random Forest for Acoustic Scene Classification. in 2018 IEEE International Conference on Multimedia and Expo, ICME 2018. vol. 2018-July, 8486578, IEEE, Institute of Electrical and Electronics Engineers, 2018 IEEE International Conference on Multimedia and Expo, ICME 2018, San Diego, United States, 23/07/18. https://doi.org/10.1109/ICME.2018.8486578

Local Binary Pattern with Random Forest for Acoustic Scene Classification. / Abidin, Shamsiah; Xia, Xianjun; Togneri, Robert; Sohel, Ferdous.

2018 IEEE International Conference on Multimedia and Expo, ICME 2018. Vol. 2018-July IEEE, Institute of Electrical and Electronics Engineers, 2018. 8486578.

Research output: Chapter in Book/Conference paperConference paper

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AU - Sohel, Ferdous

PY - 2018/10/8

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N2 - 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.

AB - 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.

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Abidin S, Xia X, Togneri R, Sohel F. Local Binary Pattern with Random Forest for Acoustic Scene Classification. In 2018 IEEE International Conference on Multimedia and Expo, ICME 2018. Vol. 2018-July. IEEE, Institute of Electrical and Electronics Engineers. 2018. 8486578 https://doi.org/10.1109/ICME.2018.8486578