Enhancing Remote Choral Education Expirence: The Application of Machine Learning Algorithms for Real-Time Audio Synchronization and Pedagogical Feedback

Abstract

This study examines the impact of machine learning algorithms on enhancing real-time audio synchronization and pedagogical feedback in remote choral education. Utilizing data from 121 schools in China, a Random Forest Regressor analyzed critical variables including Average latency per session (ALPS), audio clarity and synchronization accuracy interaction (ACSA Interaction), lagged latency variability (LLV), and normalized performance quality Score (NPQS). Results indicate that ALPS and ACSA Interaction are particularly significant in influencing pedagogical effectiveness, while LLV and NPQS also play crucial roles in the consistency and quality of remote choral sessions. Furthermore, feedback frequency (FF) and background noise level (BNL) are also important indicators for improving remote choral education experience. The study highlights the essential role of technological enhancements in remote education and suggests further improvements in network stability and audio processing technologies to optimize remote learning outcomes.