TY - JOUR
T1 - Music emotion recognition using PSO-based fuzzy hyper-rectangular composite neural networks
AU - Chin, Yu Hao
AU - Hsieh, Yi Zeng
AU - Su, Mu Chun
AU - Lee, Shu Fang
AU - Chen, Miao Wen
AU - Wang, Jia Ching
N1 - Publisher Copyright:
© The Institution of Engineering and Technology.
PY - 2017/9/1
Y1 - 2017/9/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85028629441&partnerID=8YFLogxK
U2 - 10.1049/iet-spr.2016.0021
DO - 10.1049/iet-spr.2016.0021
M3 - 期刊論文
AN - SCOPUS:85028629441
SN - 1751-9675
VL - 11
SP - 884
EP - 891
JO - IET Signal Processing
JF - IET Signal Processing
IS - 7
ER -