Human emotion is one of the most complex psychophysiological phenomena and has been reported to be affected significantly by music listening. It is supposed that there is an intrinsic relationship between human emotion and music, which can be modeled and predicted quantitatively in a supervised manner. Here, a heuristic clustering analysis is carried out on large-scale free music archive to derive a genre-diverse music library, to which the emotional response of participants is measured using a standard protocol, consequently resulting in a systematic emotion-to-music profile. Eight machine learning methods are employed to statistically correlate the basic sound features of music audio tracks in the library with the measured emotional response of tested people to the music tracks in a training set and to blindly predict the emotional response from sound features in a test set.

This study found that nonlinear methods are more robust and predictable but considerably time-consuming than linear approaches. The neural networks have strong internal fittability but are associated with a significant overfitting issue. The support vector machine and Gaussian process exhibit both high internal stability and satisfactory external predictability in all used methods; they are considered as promising tools to model, predict and explain the intrinsic relationship between human emotion and music. The psychological basis and perceptional implication underlying the built machine learning models are also discussed to find out the key music factors that affect human emotion.

人类情绪是最复杂的心理生理现象之一，据报道，听音乐会显着影响情绪。假设人类情感和音乐之间存在内在关系，可以以监督的方式对其进行建模和定量预测。在这里，对大规模免费音乐档案进行启发式聚类分析，以得出一个流派多样化的音乐库，使用标准协议测量参与者的情绪反应，从而产生系统的情绪到音乐的配置文件.

这项研究发现，非线性方法比线性方法更稳健、更可预测，但相当耗时。神经网络具有很强的内部拟合度，但存在严重的过拟合问题。支持向量机和高斯过程在所有使用的方法中都表现出较高的内部稳定性和令人满意的外部可预测性；它们被认为是建模、预测和解释人类情感与音乐之间内在关系的有前途的工具。还讨论了构建的机器学习模型背后的心理基础和感知含义，以找出影响人类情感的关键音乐因素。