中国民歌多情感识别及情感变化规律分析研究

Recognition of Emotions and Analysis of Emotional Changes in Chinese Folk Songs

【目的】采用数字化手段实现中国民歌情感的自动识别,探索民歌情感脉络特征及波动模式。【方法】基于音乐领域通用的Hevner情感模型,引入外部汉语知识对情感词进行语义增强,通过语义距离计算实现人工标注标签的自动映射;构建多模态多情感识别模型MMERM,融合歌词与音频特征实现情感自动标注;将模型迁移至片段歌曲情感识别任务,识别民歌情感变化,对情感脉络特征与波动模式进行统计分析与可视化。【结果】在情感识别方面,语义增强与映射有效提升了标签语义的集中性与区分度,MMERM在粗细粒度歌曲上均有较好表现,粗粒度歌曲上识别精度达82.29%;在规律分析方面,民歌首尾情感脉络呈现[轻盈]→[悲伤,神圣]的变化趋势,波动模式与西方音乐存在明显差异。【局限】民歌信息不足,未对不同时空下的民歌情感特征进行分析。【结论】本文提出的研究方案从数字人文视角为传统音乐领域提供了新的研究范式。

[Objective]This paper aims to achieve the automatic recognition of rich emotions in Chinese folk songs and to explore their emotional context and fluctuation patterns digitally.[Methods]We adopted Hevner’s emotion model in the field of music and introduced external Chinese knowledge for the semantic enhancement of emotion words.The automatic mapping of artificially labelled tags is then realized by semantic distance calculation.We constructed a multimodal multitag emotion recognition model(MMERM)that fuses features of lyrics and audios for automatic emotion recognition.The model is also transferred to recognize changes of emotions in songs,based on which statistical analysis and visualization of emotional context and fluctuation patterns can be conducted.[Results]The semantic enhancement and mapping effectively improve the concentration and differentiation of tags in emotion recognition.MMERM performs well on both complete songs and fragments,with a precision of 82.29%.Regularity analysis indicates a changing trend of lightness to sadness and sacredness from the beginning to the end of the songs.Furthermore,the fluctuation pattern of Chinese folk songs is found to differ remarkably from that of Western music.[Limitations]The information of folk songs is insufficient,and emotional characteristics under different temporal and spatial conditions are not analyzed.[Conclusions]This paper provides a new paradigm for the research of traditional music from the perspective of digital humanities.