Comparative Analysis of Music-Based Interventions for Mental Health Education Among College Students: Insights from LSTM Neural Network Models for Music Majors in China

Abstract

Thus, this research focuses on assessing the appropriateness of Long Short-Term Memory (LSTM) neural network models in measures to determine the efficacy of different music-based intervention programmes to address fοr mental health issues among college students, especially music majors in China. The interventions discussed refer to face-to-face interventions in MT, technology-enhanced MT, as well as virtual MT applications. The performance of these approaches is then evaluated on some standards such as, access, size, interaction, and perpetuity. Live music-based therapy has relatively high TFA and LTC, but its application is relatively expensive and influenced by availability. Technology-based music interventions, though more engaging and feasible are more standardized and thus more appropriate for acute mood regulation needs. The major advantage arises from virtual music therapy incorporating live interaction; nevertheless, scalability is hampered by requirements that necessitate engagement of the therapist at real time. Based on the LSTM models, this study is also able to provide patterns and comparisons among these approaches and provides a big data driven perspective of improving mental health education approach for different groups of college students.

Keywords:  Music-Based Interventions; Mental Health Education; LSTM Neural Networks; College Students in China; Comparative Analysis