With the development of deep neural networks, automatic music composition has made great progress. Although emotional music can evoke listeners' different auditory perceptions, only few research studies have focused on generating emotional music. This paper presents EmotionBox -a music-element-driven emotional music generator based on music psychology that is capable of composing music given a specific emotion, while this model does not require a music dataset labeled with emotions as previous methods. In this work, pitch histogram and note density are extracted (...) as features that represent mode and tempo, respectively, to control music emotions. The specific emotions are mapped from these features through Russell's psychology model. The subjective listening tests show that the Emotionbox has a competitive performance in generating different emotional music and significantly better performance in generating music with low arousal emotions, especially peaceful emotion, compared with the emotion-label-based method. (shrink)

We examined liking for happy- and sad-sounding music as a function of exposure, which varied both in quantity (number of exposures) and in quality (focused or incidental listening). Liking ratings were higher for happy than for sad music after focused listening, but similar after incidental listening. In the incidental condition, liking ratings increased linearly as a function of exposure. In the focused condition, liking ratings were an inverted U-shaped function of exposure, with initial increases in liking (after 2 exposures) followed (...) by decreases (after 8 or 32 exposures). The results documented that: (1) sad music is liked as much as happy music in some instances; (2) frequency of exposure causes both familiarity (positive) and over-familiarity (negative) effects; and (3) effects of exposure on liking differ for focused and incidental listening. (shrink)