A novel learning framework for vocal music education: an exploration of convolutional neural networks and pluralistic learning approaches

Abstract

Technology has influenced education, business, and communication in the last several years, changing many facets of human existence. Many industries have seen radical changes due to the quick development of technology, especially in artificial intelligence, machine learning, data analytics, and the Internet of things (IoT). This research explores vocal music teaching against the backdrop of the twenty-first century educational reforms seeking innovation and pursuing new teaching methodologies. The goal is to develop an autonomous learning framework reinforced by machine learning techniques that use conventional neural networks. The present study aims to implement machine learning algorithms in education, because esthetic education promotes excellence and humanistic attributes in educational environments, and art is essential in social activities. The main goal is to provide a framework that enables music education majors to access a customizable learning environment by integrating a pluralistic network approach and the strength of conventional neural networks. Music education constitutes an integral facet of quality, ideological, and moral education within general colleges and universities. The mission of music education, cultivating competent teachers for various Chinese institutions, necessitates a comprehensive understanding of students’ learning patterns, thinking characteristics, and behavioral phenomena. This study analyzes these facets across diverse levels and perspectives, employing empirical generalization, literature synthesis, logical reasoning, and action research methodologies. The research findings underscore an accuracy rate of 97.4%, emphasizing the augmentation of students' independent learning abilities in vocal music education at higher education institutions. The proposed approach outperforms machine learning techniques such as RNNs, LSTMs, and DNNs. This study highlights the significance and methods for autonomous music education at colleges to improve “music education” students’ learning through “independent learning” theory in higher education music.