LSEL: A lightweight deep learning model for social-emotional classification of classical music

Background/Objectives Social-emotional learning (SEL) plays a crucial role in special education, yet current assessment approaches rely heavily on subjective teacher observation, which can be time-consuming and difficult to standardize. Music provides a meaningful medium for evaluating emotional competencies, creating an opportunity for artificial intelligence to support more objective and scalable SEL assessment. Methods We propose a lightweight social-emotional music classification model, termed LSEL, designed to identify three SEL-related competencies: Empathetic Perspective-Taking, Outlook, and Problem-Solving. LSEL utilizes 40×128 mel-frequency cepstral coefficient as input to capture core spectral–temporal characteristics relevant to SEL perception. Moreover, we provided an open-source SEM dataset for domain experts, utilizing 591 samples, which consisted of 194 Empathetic, 214 Outlook, and 183 Perspective-Taking samples, to analyze LSEL performance. Results LSEL reaching an average accuracy of 96.55 % and mAP of 99.29 % across experiments. With Cohen’s κ averaging 94.32 % and R² reaching 94.15 %, indicating high consistency with ground-truth. Per-category accuracies were similarly stable, including 96.95 % for Empathetic Perspective-Taking, 95.16 % for Outlook, and 95.36 % for Problem-Solving. Conclusions The lightweight LSEL framework offers an effective and robust solution for social-emotional music classification, supporting objective SEL assessment in educational contexts. The release of the SEM dataset further contributes to a valuable resource for advancing AI-assisted SEL research.