Abstract
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.
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