  The surprisingness of a song is an essential and seemingly subjective factor
in determining whether the listener likes it. With the help of information
theory, it can be described as the transition probability of a music sequence
modeled as a Markov chain. In this study, we introduce the concept of deriving
entropy variations over time, so that the surprise contour of each chord
sequence can be extracted. Based on this, we propose a user-controllable
framework that uses a conditional variational autoencoder (CVAE) to harmonize
the melody based on the given chord surprise indication. Through explicit
conditions, the model can randomly generate various and harmonic chord
progressions for a melody, and the Spearman's correlation and p-value
significance show that the resulting chord progressions match the given
surprise contour quite well. The vanilla CVAE model was evaluated in a basic
melody harmonization task (no surprise control) in terms of six objective
metrics. The results of experiments on the Hooktheory Lead Sheet Dataset show
that our model achieves performance comparable to the state-of-the-art melody
harmonization model.
