ABSTRACT

Making the link between human emotion and music is challenging. Our aim was to produce an efficient system that emotionally rates songs from multiple genres. To achieve this, we employed a series of online self-report studies, utilising Russell's circumplex model. The first study (n = 44) identified audio features that map to arousal and valence for 20 songs. From this, we constructed a set of linear regressors. The second study (n = 158) measured the efficacy of our system, utilising 40 new songs to create a ground truth. Results show our approach may be effective at emotionally rating music, particularly in the prediction of valence.

摘要

在人类情感和音乐之间建立联系是具有挑战性的。我们的目标是制作一个有效的系统，可以对多种类型的歌曲进行情感评分。为了实现这一点，我们采用了一系列在线自我报告研究，利用罗素的循环模型。第一项研究 ( n  = 44) 确定了映射到 20 首歌曲的唤醒和效价的音频特征。由此，我们构建了一组线性回归量。第二项研究 ( n  = 158) 测量了我们系统的功效，利用 40 首新歌曲来创建基本事实。结果表明，我们的方法在对音乐进行情感评价方面可能有效，尤其是在效价预测方面。