Abstract
Music emotion experience is a rather subjective and personalized issue. Therefore, we previously developed a personalized music recommendation system called MemoMusic to navigate listeners to more positive emotional states based not only on music emotion but also on possible memories aroused by music. In this paper, we propose to extend MemoMusic with automatic music generation based on an LSTM network, which can learn the characteristic of a tiny music clip with particular Valence and Arousal values and predict a new music sequence with similar music style. We call this enhanced system MemoMusic Verison 2.0. For experiment, a new dataset of 177 music in MIDI format was collected and labelled using the Valence-Arousal model from three categories of Classical, Popular, and Yanni music. Experimental results further demonstrate that memory is an influencing factor in determining perceived music emotion, and MemoMusic Version 2.0 can moderately navigate listeners to better emotional states.
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