An objective evaluation method of vocal singing effect based on artificial intelligence technology

Abstract

Abstract The continuous progress of artificial intelligence technology has shown great potential for application in several fields, especially music. The research direction of Objective Evaluation of Vocal Singing Effectiveness uses advanced technologies to analyze and assess a singer’s performance across multiple dimensions, including pitch, rhythm, and timbre, and is highly valuable. Building an accurate and fair evaluation system faces many challenges, including how to accurately capture and analyze the subtle changes in the voice and synthesize the effects of different musical elements on the quality of the performance. This requires researchers to explore music theory, sound analysis techniques, and artificial intelligence algorithms, and develop a new methodology that can comprehensively evaluate the effectiveness of vocal singing. This paper constructs a complete set of vocal singing evaluation models by analyzing acoustic feature extraction, Hidden Markov Model, and Generalized Regression Radial Basis Function Network in detail. The study adopts a logarithmic Mel spectrum for acoustic feature extraction to effectively capture the essential attributes of the singing voice. Hidden Markov models and mixed Gaussian models are used to model the sound signal, improving phoneme recognition accuracy. Accurate singing effect was evaluated using a generalized regression radial basis function network. In this article, the accuracy of this evaluation method in terms of pitch, rhythm, and timbre reached 95%, 93%, and 89%, respectively, demonstrating high evaluation consistency and reliability. The research method provides a new objective evaluation tool for vocal singing effects, which is valuable for vocal teaching and self-practice.