Wen Xin, Huang Zhengxi, Sun Zaoyi, Xu Liang
Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
College of Education, Zhejiang University of Technology, Hangzhou, China.
Psych J. 2022 Oct;11(5):673-683. doi: 10.1002/pchj.510. Epub 2021 Dec 12.
This study examines perceptions of music depth by exploring its relationships to different music features. First, a correlation analysis shows that the perceived depth of music is negatively correlated with valence and arousal and is also related to different music features, including tempo, Mel-frequency cepstrum coefficients, chromagrams, spectral centroids, spectral bandwidth, spectral contrast, spectral flatness, spectral roll-off, and tonal centroid features. Applying machine learning methods, we find that selected music features can predict perceptions of music depth, and a random forest regression (RFR) is found to perform best in this study. Finally, a feature importance analysis shows that the principal component of spectral contrast dominates the RFR-based music depth recognition model, showing that deep music usually has clear and narrow-band audio signals.
本研究通过探索音乐深度与不同音乐特征之间的关系来考察对音乐深度的认知。首先，相关性分析表明，音乐的感知深度与效价和唤醒度呈负相关，并且还与不同的音乐特征有关，包括节奏、梅尔频率倒谱系数、色度图、谱质心、谱带宽、谱对比度、谱平坦度、谱滚降和音调质心特征。应用机器学习方法，我们发现所选的音乐特征可以预测对音乐深度的认知，并且在本研究中发现随机森林回归（RFR）表现最佳。最后，特征重要性分析表明，谱对比度的主成分主导了基于RFR的音乐深度识别模型，这表明深沉的音乐通常具有清晰且窄带的音频信号。
