Open AccessMusic emotion recognition using PSO‐based fuzzy hyper‐rectangular composite neural networks
Author(s) -
Chin YuHao,
Hsieh YiZeng,
Su MuChun,
Lee ShuFang,
Chen MiaoWen,
Wang JiaChing
Publication year - 2017
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2016.0021
Subject(s) - particle swarm optimization , computer science , fuzzy logic , artificial neural network , artificial intelligence , fuzzy rule , fuzzy set , set (abstract data type) , rough set , feature extraction , pattern recognition (psychology) , machine learning , programming language
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.