  With the development of deep neural networks, automatic music composition has
made great progress. Although emotional music can evoke listeners' different
emotions and it is important for artistic expression, only few researches have
focused on generating emotional music. This paper presents EmotionBox -an
music-element-driven emotional music generator that is capable of composing
music given a specific emotion, where this model does not require a music
dataset labeled with emotions. Instead, pitch histogram and note density are
extracted as features that represent mode and tempo respectively to control
music emotions. The subjective listening tests show that the Emotionbox has a
more competitive and balanced performance in arousing a specified emotion than
the emotion-label-based method.
