Music Emotion Fuzzy Computing Model Based on Evolving Kernel Clustering
L Lan-Lan, ZHOU Chang-Le
Department of Cognitive Science,School of Information Science and Technology,Xiamen University,Xiamen 361005 Fujian Key Laboratory of the Brain-Like Intelligent Systems,Xiamen 361005
Abstract Music emotion computing is a complex problem of emotion representation, which has multi-level and multi-dimensional structure. Its characteristics of fuzziness, subtleness and diversity result in the inefficiency of traditional methods. In order to improve recognition accuracy, firstly, the non-linear mapping of Gaussian radial basis function is used to identify, extract and magnify more details. Then, six key emotional features are extracted, by analyzing Chinese Guqin music in depth, and the fuzzy classification model for music emotion is constructed based on kernel clustering evolutionary algorithm. Moreover, aiming at the shortcoming of setting uniform cluster radius threshold in algorithm, the corresponding optimization strategy is proposed based on ACO. Finally, the optimized model is compared with Beyes classification model, and the experimental results show that the proposed method is effective.
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2012, Vol. 25 
