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Coherence and interestingness are two criteria for evaluating the performanceof melody harmonization, which aims to generate a chord progression from asymbolic melody. In this study, we apply the concept of orderless NADE, whichtakes the melody and its partially masked chord sequence as the input of theBiLSTM-based networks to learn the masked ground truth, to the trainingprocess. In addition, the class weights are used to compensate for somereasonable chord labels that are rarely seen in the training set. Consistentwith the stochasticity in training, blocked Gibbs sampling with proper numbersof masking/generating loops is used in the inference phase to progressivelytrade the coherence of the generated chord sequence off against itsinterestingness. The experiments were conducted on a dataset of 18,005melody/chord pairs. Our proposed model outperforms the state-of-the-art systemMTHarmonizer in five of six different objective metrics based on chord/melodyharmonicity and chord progression. The subjective test results with more than100 participants also show the superiority of our model.

Melody Harmonization Using Orderless NADE, Chord Balancing, and Blocked Gibbs Sampling