LOCAL BINARY PATTERN WITH RANDOM FOREST FOR ACOUSTIC SCENE CLASSIFICATION

被引：0

作者：

Abidin, Shamsiah ^{[1
]}

Xia, Xianjun ^{[1
]}

Togneri, Roberto ^{[1
]}

Sohel, Ferdous ^{[2
]}

机构：

[1] Univ Western Australia, Sch Elect Elect & Comp Engn, Perth, WA, Australia

[2] Murdoch Univ, Sch Engn & Informat Technol, Perth, WA, Australia

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME) | 2018年

关键词：

acoustic scene; local binary patterns; feature extraction; time-frequency analysis; fusion; random forest;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页数：6

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