Voice Recognition and Evaluation of Vocal Music Based on Neural Network

Abstract

Artistic voice is the artistic life of professional voice users. In the process of selecting and cultivating artistic performing talents, the evaluation of voice even occupies a very important position. Therefore, an appropriate evaluation of the artistic voice is crucial. With the development of art education, how to scientifically evaluate artistic voice training methods and fairly select artistic voice talents is an urgent need for objective evaluation of artistic voice. The current evaluation methods for artistic voices are time-consuming, laborious, and highly subjective. In the objective evaluation of artistic voice, the selection of evaluation acoustic parameters is very important. Attempt to extract the average energy, average frequency error, and average range error of singing voice by using speech analysis technology as the objective evaluation acoustic parameters, use neural network method to objectively evaluate the singing quality of artistic voice, and compare with the subjective evaluation of senior professional teachers. In this paper, voice analysis technology is used to extract the first formant, third formant, fundamental frequency, sound range, fundamental frequency perturbation, first formant perturbation, third formant perturbation, and average energy of singing acoustic parameters. By using BP neural network methods, the quality of singing was evaluated objectively and compared with the subjective evaluation of senior vocal professional teachers. The results show that the BP neural network method can accurately and objectively evaluate the quality of singing voice by using the evaluation parameters, which is helpful in scientifically guiding the selection and training of artistic voice talents.

Construction of an Art Education Teaching System Assisted by Artificial Intelligence

“…Traditional vocal evaluation mainly relies on the subjective judgment of professional judges, while neural network-based vocal speech evaluation technology can provide more accurate and comprehensive evaluation through objective analysis of vocal signals. Neural networks can learn the evaluation criteria and preferences of professional judges, thereby achieving automatic evaluation of the singer's voice quality, skill application, emotional expression, and other aspects [7]. Mei et al [8] explore the possibility of constructing art theory from the perspective of art images based on AI assisted analysis, and analyze its impact on art research and creation.…”