Jiale Zhao¹ and Soroush Mirzaee², ¹USA, ²California State University Long Beach, USA

This paper addresses the challenge of creating an affordable and effective guitar learning system. Traditional guitar learning methods rely heavily on teacher-student interaction, which can be limited in terms of feedback and accessibility [10]. To solve this problem, we propose a system that uses membrane buttons on the guitar fretboard to detect user input, combined with machine learning to provide real-time feedback and corrections. The system converts raw guitar signals into a readable format and integrates with an application to enhance the learning experience. Key technologies include RP2040 for signal conversion and machine learning for input analysis. Challenges such as signal accuracy and real-time feedback were addressed by using membrane buttons, which are more accurate and cost-effective compared to other methods like video detection or audio analysis. The system was tested in various scenarios, demonstrating its potential to provide an interactive, accessible, and personalized guitar learning experience that can improve how students learn the instrument.

Adaptive, Assist, Guitar Learning, Music Creation, Machine Learning