Learning Classical Music Through AI-Powered Practice Tools

Abstract

This study explores the utilization of AI-powered practice tools that combine machine learning algorithms, real-time feedback systems, and speech and audio recognition technologies to enhance the acquisition of classical music. Classical music learning often lacks real-time, personalized feedback that impedes student progress and engagement. This paper aims to measure how AI tools improve key musical performance metrics - pitch accuracy, rhythm consistency, and tempo regulation - relative to traditional learning approaches. Machine learning algorithms process real-time learner performances to generate customized feedback and correction; speech and audio recognition ensure accurate assessment of voice and instrumental practice. The study also explores the impact of these tools on learner engagement, motivation, and skill retention. Results indicate significant improvements in pitch accuracy, rhythm consistency, and tempo regulation, with participants using AI tools reporting higher motivation, engagement, and long-term retention of learned skills. This research highlights the potential of AI-powered practice tools to revolutionize classical music education, offering more personalized, interactive, and efficient learning experiences for students.