EXPLAINABLE AI FOR CREATIVE MUSIC EDUCATION: VISUALIZING SOUND, PATTERNS, AND HARMONY FOR STUDENT LEARNING

Abstract [English]

In recent years, explainable artificial intelligence (XAI) has gained popularity as a means to enhance various fields, including education. This paper examines the use of XAI techniques in innovative music instruction. The aim is to employ AI-powered models to visualise sound, rhythms, and harmony, which will let students grasp musical compositions more readily. Traditional song training is based closely on idea and bodily analysis, which gives little opportunity for hands-on gaining knowledge of. Adding XAI to song training equipment will help us to allow students to learn by using doing. This might permit extra hands-on, individualised learning for pupils. Creating photographs that depict complex musical systems encourages college students to engage with sound in a proper procedure, as a result improving their writing and listening capabilities. The method provides clear, real-time remarks on musical works highlighting the hyperlinks between concord, rhythm, and track. This method teaches college students the vital rhythms that underlie music whilst letting them experiment with own creations. Researchers are investigating how efficaciously this XAI approach enables students to understanding music principle, be innovative, and be inspired by using musical sports. Instructors assist students in grasp how diverse additives of track have interaction by way of photographs of musical styles, consequently enabling greater exciting and attractive getting to know. The findings, for instance, indicate using XAI in music courses helps to make learning more enjoyable, simpler, and customisable to fit the requirements of every student.