Abstract
There were a lot of psychological music experiments and models but there were few psychological rhythm experiments and models. There were a lot of physiological music experiments but there were few physiological music models. There were few physiological rhythm experiments but there was no physiological rhythm model. We proposed a physiological rhythm model to fill this gap. Twenty-two participants, 4 drum loops as stimuli, and electrocardiogram (ECG) were employed in this work. We designed an algorithm to map tempo, complexity, and energy into two heart rate variability (HRV) measures, the standard deviation of normal-to-normal heartbeats (SDNN) and the ratio of low- and high-frequency powers (LF/HF); these two measures form the physiological valence/arousal plane. There were four major findings. Initially, simple and loud rhythms enhanced arousal. Secondly, the removal of fast and loud rhythms decreased arousal. Thirdly, fast rhythms increased valence. Finally, the removal of fast and quiet rhythms increased valence. Our work extended the psychological model to the physiological model and deepened the musical model into the rhythmic model. Moreover, this model could be the rules of automatic music generating systems.
Highlights
The relation of music to emotion has been studied for decades and the literature is fruitful [1]
One of the physiological actions, heart rate variability (HRV), which is controlled by the autonomic nervous system (ANS), is tightly connected with emotions [4]
Musical emotions change with psychophysiological measures and musical features [8], whilst three basic questions are highlighted [9]: how do musical features evoke emotions; how do actions involved in musical emotions progress; and which actions and brain processes are involved in musical emotions
Summary
The relation of music to emotion has been studied for decades and the literature is fruitful [1]. There exist a lot of psychological models between music and emotion [2], but the physiological models between music and emotion are limited [3]. We had analyzed the relationship between musical rhythms and HRV [5] and built two heuristic models [6, 7]. The mappings between music space and emotion space [13, 14] employ the following synonyms: music mood detection [15], music emotion measurement [12], characterization [12], recognition [16, 17], classification [12, 18], predicting [19], or modeling [20]; the review articles demonstrate the fruits of experts’ interests [16,17,18,19,20]
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