Beyond intensity: Spectral features effectively predict music-induced subjective arousal

Abstract

Emotions in music are conveyed by a variety of acoustic cues. Notably, the positive association between sound intensity and arousal has particular biological relevance. However, although amplitude normalization is a common procedure used to control for intensity in music psychology research, direct comparisons between emotional ratings of original and amplitude-normalized musical excerpts are lacking.

In this study, 30 nonmusicians retrospectively rated the subjective arousal and pleasantness induced by 84 six-second classical music excerpts, and an additional 30 nonmusicians rated the same excerpts normalized for amplitude. Following the cue-redundancy and Brunswik lens models of acoustic communication, we hypothesized that arousal and pleasantness ratings would be similar for both versions of the excerpts, and that arousal could be predicted effectively by other acoustic cues besides intensity.

Although the difference in mean arousal and pleasantness ratings between original and amplitude-normalized excerpts correlated significantly with the amplitude adjustment, ratings for both sets of excerpts were highly correlated and shared a similar range of values, thus validating the use of amplitude normalization in music emotion research. Two acoustic parameters, spectral flux and spectral entropy, accounted for 65% of the variance in arousal ratings for both sets, indicating that spectral features can effectively predict arousal. Additionally, we confirmed that amplitude-normalized excerpts were adequately matched for loudness. Overall, the results corroborate our hypotheses and support the cue-redundancy and Brunswik lens models.

Keywords:

• Music
• Emotion
• Intensity
• Arousal
• Brunswik lens model

We thank Andreas Gartus for his technical assistance, Helmut Leder for providing laboratory space, and Stephen McAdams, Bruno Giordano, and two anonymous reviewers for their comments and suggestions.

This research was supported by European Research Council (ERC) Advanced Grant SOMACCA [grant number 230604] and a University of Vienna startup grant to W.T.F.