Optimization of vocal singing training method based on intelligent big data technology

Abstract

With the advancement of science and technology, the relationship between vocal singing training methods and big data technology is getting closer and closer. The application of training methods is a means of expressing vocal compositions. Emotional expression is the ultimate purpose and meaning of method optimization. Training methods and emotional expression are equally important in vocal singing, and both are indispensable. In this paper, a VSSA-separation model is established based on the Vocal Signal Separation Algorithm (VSSA). Focus on optimizing the training method of vocal singing. Through in-depth discussion of existing works, I try to find its inner influence and outer connection. In order to better understand the grasp and application of vocal singing method and emotional expression balance. It can be seen from the calculation that the image shaping in the song language accounts for 83% of the optimization of the training method. Inverse word processing in song language has a different proportion of the optimization of the training method. It accounts for 71% of English operas. It accounts for only 13% of Chinese operas. Artistic effects have the highest average optimization difficulty among the training methods. Its optimization difficulty is as high as 89%.

Keywords

• big data technology
• vocal singing
• training method
• vocal signal separation algorithm
• vssa-separation model
• 97q60