安阳幼儿师范高等专科学校（Anyang Preschool Teachers College）简称安阳幼专，坐落于河南省安阳市，是经河南省人民政府批准、教育部备案的公办全日制普通高等院校。学校前身是1978年创办的安阳市师范学校，1988年更名为安阳市第一师范学校，2002年更名为安阳幼儿师范学校，2012年2月升格为公办全日制幼儿师范高等专科学校。截止2020年9月，学校总占地面积484亩，规划建筑总面积约42.1万平方米。全日制在校生7000余人；教职工402人，其中专任教师346人，副高级以上职称86人，硕士研究生123人。设有6个教学系部、17个行政教辅机构和2个附属幼儿园，8个高职专科专业。
Abstract Objective Faced with heavy academic tasks and interpersonal challenges, college students generally bear persistent psychological pressure, presenting impetuous and fragile mental states and emerging mental health problems. Traditional university fine arts teaching focuses only on skill cultivation and ignores psychological guidance. This paper explores the effective infiltration mode of mental health education in art teaching, aiming to relieve students’ learning psychological pressure, adjust negative mental states and promote sound mental health development. Subjects and Methods This study takes college students receiving fine arts courses as research subjects, adopting classroom observation, teaching intervention and comparative questionnaire surveys. It collects data on students’ learning psychological pressure, emotional mental states and mental health levels before and after mental health infiltration teaching, and analyzes the practical adjustment effect of art education on students’ psychology. Results Reasonable infiltration of mental health education in fine arts teaching effectively relieves students’ cumulative academic psychological pressure, alleviates anxious and confused mental states generated in study and life, exerts positive emotional edification, and significantly improves students’ overall mental health and psychological resilience. Conclusions Fine arts teaching has unique emotional healing and psychological regulation value. Scientific penetration of mental health education can continuously ease college students’ daily psychological pressure, cultivate stable and positive mental states, make up for the deficiencies of conventional psychological education, and provide effective paths for optimizing college students’ mental health literacy. Corresponding Author Shujing Yang, Anyang Preschool Teachers College, Anyang, Henan, 456150, China.
Music emotion recognition (MER) is essential for affective computing and intelligent recommendation, yet solo instrumental music—particularly violin performance—remains underexplored due to the absence of lyrics, the subtlety of emotional expression, and the lack of dedicated datasets. To address these gaps, this study adopts the VioMusic dataset, a publicly available violin performance dataset with phrase-level Valence-Arousal (VA) annotations, comprising approximately 7.5 h of recordings and 1926 segmented phrases. Furthermore, this study proposes a CL-HuBERT (Contrastive Learning with HuBERT) framework that performs domain-adaptive continued pre-training of a HuBERT encoder via contrastive learning on unlabeled violin audio, followed by supervised fine-tuning for valence-arousal prediction. Experimental results demonstrate that the proposed framework achieves superior performance, with Pearson correlation coefficients of 0.718 for arousal and 0.701 for valence, and MAE values of 0.089 and 0.095, respectively, under the optimal feature fusion setting. Systematic comparisons of audio features, feature fusion strategies, data preprocessing methods, and deep feature extractors further validate the effectiveness of the proposed approach. This study provides an effective self-supervised contrastive learning framework and establishes a strong benchmark for solo instrumental MER.
