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Evaluation of Vocal Music Artistic Talent Based on Emotional Features

机译：基于情感特征的声乐艺术人才评价

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How to evaluate music artistic talent fast and efficiently is one of the hot topics at present. Music talent include the ability to perceive music, the ability to play, and the ability to understand and create music. In this paper, a novel framework based on music artistic talent evaluation algorithm is proposed according to the music data flow. Along with the occurrence of music art events, the emotional features in growth data flow exist abrupt phenomenon as well. We monitor the emotion features change in real-time, in order to exploit music artistic ability. The emotional feature model was built based on the algorithm of frequent pattern excavation and mutual information. The emotional features in data flow were extracted through this model. The music artistic talent was evaluated and the children's growth art events were combined by the heuristic affinity propagation clustering algorithm. The results show that this algorithm can effectively excavate the music artistic talent. It can guarantee the real-time online processing in both speed and accuracy requirements.
机译：如何快速，有效地评价音乐艺术人才是当前的热点问题之一。音乐天分包括感知音乐的能力，演奏的能力以及理解和创作音乐的能力。根据音乐数据流，提出了一种基于音乐艺术才华评估算法的新颖框架。随着音乐艺术事件的发生，成长数据流中的情感特征也存在突变现象。我们实时监控情绪特征的变化，以开发音乐的艺术能力。基于频繁模式挖掘和互信息算法建立情感特征模型。通过该模型提取了数据流中的情感特征。通过启发式亲和力传播聚类算法对音乐艺术才能进行评估，并结合儿童成长艺术活动。结果表明，该算法可以有效挖掘音乐艺术人才。它可以在速度和准确性要求上保证实时在线处理。

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