Application of Machine Learning Technology in Classical Music Education

Introduction

In machine learning, ‘data mining’ and ‘data analysis’ are similar terms that connote a process of recognizing meaningful, effective, special, and valuable facts from abundant data (Gupta et al., 2021). Before the application of information technology, people could only mine and analyze data manually. In the era of data, information has seen extraordinary growth. Individuals are continuously generating and leveraging data to function and thrive in their day-to-day lives. Through a combination of data storage technology and advanced machine learning algorithms, the field of data mining and analysis has been greatly expanded (Domashova & Zabelina, 2021). Data can be read and written efficiently through the current efficient data storage technology (Gupta et al., 2012). Afterward, data mining and analysis are optimized through the deployment of knowledge discovery, data statistics, and machine learning technology. The utilization of such technology brings forth undeniable advantages in terms of data processing and evaluation (Islam et al., 2021).

Machine learning plays a crucial role in music education, primarily as follows. First, recent developments in machine learning and artificial intelligence (AI) have the potential to optimize the aptitude of music teachers (Walker, 2021). AI offers a viable solution for replacing staff members who do not specialize in music education, thereby elevating the capabilities of existing music educators. By utilizing AI, music teachers acquire the advantage of an effective supplementary aid, resulting in an improved standard of expertise across the board. Students and parents will continue to improve their recognition of intelligent machines. In addition, with the help of artificial intelligence, music teachers can carry out self-study more efficiently and conveniently, thus continuously optimizing the teachers’ level. Second, it can promote the improvement of teachers’ teaching quality and efficiency. Relying on AI and big data analysis, teachers can quickly understand issues such as the students’ learning level or background. In this way, teachers can quickly become effective carry out effective teaching for students. Meeting each student’s educational needs can improve teaching quality and efficiency. Third, it can enhance students’ learning efficiency. Music learning is not always fun and to master certain music skills, learners must invest considerable time and energy; however, not every learner persists. The introduction of artificial intelligence can mobilize the students’ subjective initiative in learning music, help them realize the shortcomings in their own learning, urge them to learn, and effectively improve their learning efficiency.

Machine learning is an interdisciplinary field, comprising elements of science, psychology, biology, systems science, cognitive science, and information science. Through incorporating robotic technology, classical music education can be advanced with features such as recognition of instruments, feature extraction, and recognizing classical tunes. Consequently, intelligent instruments gain additional useful features, creating a personalized learning environment. Furthermore, machine learning technology enables observation of classroom instruction, analysis of melody and rhythm, making evaluations of teaching proficiency more precise and accurate, ultimately creating an atmosphere to enhance instructors’ creativity while they use artificial intelligence to innovatively present the discipline through modern means.