  Generating melody from lyrics is an interesting yet challenging task in the
area of artificial intelligence and music. However, the difficulty of keeping
the consistency between input lyrics and generated melody limits the generation
quality of previous works. In our proposal, we demonstrate our proposed
interpretable lyrics-to-melody generation system which can interact with users
to understand the generation process and recreate the desired songs. To improve
the reliability of melody generation that matches lyrics, mutual information is
exploited to strengthen the consistency between lyrics and generated melodies.
Gumbel-Softmax is exploited to solve the non-differentiability problem of
generating discrete music attributes by Generative Adversarial Networks (GANs).
Moreover, the predicted probabilities output by the generator is utilized to
recommend music attributes. Interacting with our lyrics-to-melody generation
system, users can listen to the generated AI song as well as recreate a new
song by selecting from recommended music attributes.
