We describe the development of a computational model predicting listener-perceived expressions of music in branding contexts. Representative ground truth from multi-national online listening experiments was combined with machine learning of music branding expert knowledge, and audio signal analysis toolbox outputs. A mixture of random forest and traditional regression models is able to predict average ratings of perceived brand image on four dimensions. Resulting cross-validated prediction accuracy (R²) was Arousal: 61%, Valence: 44%, Authenticity: 55%, and Timeliness: 74%. Audio descriptors for rhythm, instrumentation, and musical style contributed most. Adaptive sub-models for different marketing target groups further increase prediction accuracy.

中文翻译：

---

一种用于预测品牌场景中感知音乐表达的计算模型

我们描述了一个计算模型的开发，该模型可以预测品牌背景中听众感知的音乐表达。来自多国在线收听实验的代表性地面实况与音乐品牌专家知识的机器学习和音频信号分析工具箱输出相结合。随机森林和传统回归模型的混合能够在四个维度上预测感知品牌形象的平均评分。结果交叉验证的预测准确度 (R²) 为 Arousal: 61%, Valence: 44%, Authenticity: 55%, and Timeliness: 74%。节奏、乐器和音乐风格的音频描述符贡献最大。针对不同营销目标群体的自适应子模型进一步提高了预测准确性。