Memo2496

Music emotion recognition delineates and categorises the spectrum of emotions expressed within musical compositions by conducting a comprehensive analysis of fundamental attributes, including melody, rhythm, and timbre. This task is pivotal for the tailoring of music recommendations, the enhancement of music production, the facilitation of psychotherapeutic interventions, and the execution of market analyses, among other applications. The cornerstone is the establishment of a music emotion recognition dataset annotated with reliable emotional labels, furnishing machine learning algorithms with essential training and validation tools, thereby underpinning the precision and dependability of emotion detection. The Music Emotion Dataset with 2496 Songs (Memo2496) dataset, comprising 2496 instrumental musical pieces annotated with valence-arousal (VA) labels and acoustic features, is introduced to advance music emotion recognition and affective computing. The dataset is meticulously annotated by 30 music experts proficient in music theory and devoid of cognitive impairments, ensuring an unbiased perspective. The annotation methodology and experimental paradigm are grounded in previously validated studies, guaranteeing the integrity and high calibre of the data annotations.

音乐情感识别（Music Emotion Recognition）指通过对旋律、节奏、音色等核心属性开展全面分析，对音乐作品所传递的各类情感进行刻画与分类。该任务对于定制化音乐推荐、音乐制作优化、心理治疗干预辅助以及市场分析等诸多应用场景而言至关重要。其核心基石在于构建带有可靠情感标签的音乐情感识别数据集，为机器学习算法提供必要的训练与验证工具，进而保障情感检测的精度与可靠性。本研究推出了包含2496首器乐作品的《2496首歌曲音乐情感数据集》（Music Emotion Dataset with 2496 Songs, 简称Memo2496），该数据集附带效价-唤醒（valence-arousal, VA）标签与声学特征，旨在推动音乐情感识别与情感计算领域的发展。该数据集由30名精通音乐理论且无认知障碍的音乐专家进行精细标注，确保标注视角客观公正。其标注方法与实验范式均基于已被验证的前期研究，确保了数据标注的完整性与高质量。