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

TL;DR

This paper introduces a novel melody harmonization method combining orderless NADE, chord balancing, and Gibbs sampling, achieving superior results over existing systems in both objective metrics and subjective evaluations.

Contribution

The study presents a new approach integrating orderless NADE with class weights and Gibbs sampling for improved melody harmonization.

Findings

Outperforms state-of-the-art MTHarmonizer in 5 of 6 metrics

Achieves higher harmonicity and progression quality

Subjective tests confirm improved musicality

Abstract

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.