Adaptive vocal performance training using transfer learning and genetic algorithm: Personalization and optimization

Abstract

This study proposes an adaptive vocal performance training system based on transfer learning and genetic algorithms to address the limitations of traditional training methods, such as insufficient personalization and low efficiency. The system extracts high-level performance techniques from existing vocal datasets using transfer learning, and fine-tunes the pre-trained ResNet-50 convolutional neural network to cater to the specific needs of vocal training. A genetic algorithm is then employed to optimize the training plan, which is continuously refined through multiple iterations to meet individual student needs. Personalized training plans are provided based on the students’ voice characteristics and learning requirements. The system’s performance was evaluated, showing significant improvements in training effectiveness, with a training improvement score of 4.5 ± 0.4 and a user satisfaction score of 4.7 ± 0.3. The system also achieved a personalization score of 4.8 ± 0.2 and reduced training time to 28 ± 3 min. Compared to traditional vocal training methods, this system demonstrates superior performance in enhancing both training efficiency and effectiveness.