  Music generation research has grown in popularity over the past decade,
thanks to the deep learning revolution that has redefined the landscape of
artificial intelligence. In this paper, we propose a novel approach to music
generation inspired by musical segment concatenation methods and hash learning
algorithms. Given a segment of music, we use a deep recurrent neural network
and ranking-based hash learning to assign a forward hash code to the segment to
retrieve candidate segments for continuation with matching backward hash codes.
The proposed method is thus called Deep Segment Hash Learning (DSHL). To the
best of our knowledge, DSHL is the first end-to-end segment hash learning
method for music generation, and the first to use pair-wise training with
segments of music. We demonstrate that this method is capable of generating
music which is both original and enjoyable, and that DSHL offers a promising
new direction for music generation research.
