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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51481

Title: Music Emotion Detection Using PSO-Based Fuzzy Hyper-Rectangular Composite Neural Networks
Authors: Yu-Hao Chin
Yi-Zeng Hsieh
Mu-Chun Su
Shu-Fang Lee
Miao-Wen Chen
Jia-Ching Wang
Contributors: 國立臺灣海洋大學:電機工程學系
Date: 2017-06
Issue Date: 2018-11-30T07:50:33Z
Publisher: IET Signal Processing
Abstract: Abstract: This study proposed a novel system for recognising emotional content in music, and the proposed system is based on particle swarm optimisation (PSO)-based fuzzy hyper-rectangular composite neural networks (PFHRCNNs), which integrates three computational intelligence tools, i.e. hyper-rectangular composite neural networks (HRCNNs), fuzzy systems, and PSO. PFHRCNN is flexible to the complex data due to the fuzzy membership estimation, and an optimisation of the parameters is provided by PSO. First, raw features are extracted from each music clips. After feature extraction, a HRCNN is separately constructed for each class. Each trained HRCNN will result in a set of crisp rules. A problem associated with these generated crisp rules is that some of them are ineffective; therefore, a crisp rule is transformed into a fuzzy rule incorporated with a confidence factor. Next, PSO is adopted to simultaneously trim the rules, search a set of good confidence factors, and fine-tune the locations of the selected hyper-rectangles to increase their effectiveness. Finally, a PFHRCNN consisted of a set of fuzzy rules can be generated to recognise the emotion state of music. The experimental result shows that the proposed system has a good performance.
Relation: 11(7) pp.884 - 891
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51481
Appears in Collections:[電機工程學系] 期刊論文

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