基于核聚类进化算法的音乐情感模糊计算模型

Music Emotion Fuzzy Computing Model Based on Evolving Kernel Clustering

音乐情感计算涉及到多维度多层次结构的复杂情感表征问题,而情感本身所具有的模糊性、细微性和多样性,使得传统的情感识别方法普遍效率低下且正确率不高.为提高识别精度,首先利用高斯径向基函数进行非线性映射,来分辨、提取并放大更多的细节信息.然后通过深入剖析中国古琴乐曲,从中抽取出影响最大的六个情感特征值,并在非线性映射的基础上,构造一种基于核聚类进化算法的音乐情感模糊计算模型.在此基础上,进一步针对算法中统一设定簇半径阈值的不足,提出基于蚁群算法的规则调整策略,并进行系统实验.实验结果表明,与基于概率统计的Beyes分类方法相比,优化后的模糊计算模型具有较好的识别效果.

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.