What a deep song: The role of music features in perceived depth.

Abstract

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.