  Coherence and interestingness are two criteria for evaluating the performance
of melody harmonization, which aims to generate a chord progression from a
symbolic melody. In this study, we apply the concept of orderless NADE, which
takes the melody and its partially masked chord sequence as the input of the
BiLSTM-based networks to learn the masked ground truth, to the training
process. In addition, the class weights are used to compensate for some
reasonable chord labels that are rarely seen in the training set. Consistent
with the stochasticity in training, blocked Gibbs sampling with proper numbers
of masking/generating loops is used in the inference phase to progressively
trade the coherence of the generated chord sequence off against its
interestingness. The experiments were conducted on a dataset of 18,005
melody/chord pairs. Our proposed model outperforms the state-of-the-art system
MTHarmonizer in five of six different objective metrics based on chord/melody
harmonicity and chord progression. The subjective test results with more than
100 participants also show the superiority of our model.
