Department of Psychology, College of Education, Zhejiang University of Technology, Hangzhou, China.
Psych J. 2024 Dec;13(6):915-926. doi: 10.1002/pchj.785. Epub 2024 Jun 19.
Musical depth, which encompasses the intellectual and emotional complexity of music, is a robust dimension that influences music preference. However, there remains a dearth of research exploring the relationship between lyrics and musical depth. This study addressed this gap by analyzing linguistic inquiry and word count-based lyric features extracted from a comprehensive dataset of 2372 Chinese songs. Correlation analysis and machine learning techniques revealed compelling connections between musical depth and various lyric features, such as the usage frequency of emotion words, time words, and insight words. To further investigate these relationships, prediction models for musical depth were constructed using a combination of audio and lyric features as inputs. The results demonstrated that the random forest regressions (RFR) that integrated both audio and lyric features yielded superior prediction performance compared to those relying solely on lyric inputs. Notably, when assessing the feature importance to interpret the RFR models, it became evident that audio features played a decisive role in predicting musical depth. This finding highlights the paramount significance of melody over lyrics in effectively conveying the intricacies of musical depth.
音乐深度，包含音乐的知识和情感复杂性，是一个强大的维度，影响着音乐偏好。然而，目前还缺乏研究来探索歌词和音乐深度之间的关系。本研究通过分析从 2372 首中文歌曲的综合数据集提取的基于语言探究和词汇量的歌词特征，填补了这一空白。相关分析和机器学习技术揭示了音乐深度与各种歌词特征之间的紧密联系，例如情感词、时间词和洞察力词汇的使用频率。为了进一步研究这些关系，使用音频和歌词特征的组合作为输入构建了音乐深度预测模型。结果表明，集成音频和歌词特征的随机森林回归（RFR）模型相比仅依赖歌词输入的模型具有更优的预测性能。值得注意的是，在评估特征重要性以解释 RFR 模型时，音频特征在预测音乐深度方面起着决定性的作用。这一发现强调了旋律在有效地传达音乐深度的复杂性方面的至关重要性，超过了歌词的作用。
