Research on Music Fountain Control System Based on Feature Extraction and BP Neural Network
The current music fountain control system has problems such as single music feature recognition and high control mode consumption.Therefore,a music fountain control system with intelligent characteristics was designed using feature extraction and backpropagation networks,and its effectiveness was verified.The experimental results show that in music feature extraction,taking Song 1 as an example,the beat of the music is maintained between 0.5 and 1;The intensity of rhythm variation is maintained between 1.26 and 2.92;The third degree is maintained between 0.03 and 0.13,and the feature vectors are basically expressed.In addition,in backpropagation network validation,its recognition rate for calm emotions is the highest,at 97.34％;Compared with other algo-rithms,the highest values of algorithms A,B,C,and D are 83.35％,91.25％,87.22％,and 95.89％,respectively,which are lower than the research algorithms.On this basis,the system model validation designed in the study is well adapted to changes in mu-sic.Overall,the designed music fountain control system has practicality and can be effectively applied in practical music fountains.
featuresBP networkmusic fountain control systememotion
万方数据
维普
