  This paper studies the prediction of chord progressions for jazz music by
relying on machine learning models. The motivation of our study comes from the
recent success of neural networks for performing automatic music composition.
Although high accuracies are obtained in single-step prediction scenarios, most
models fail to generate accurate multi-step chord predictions. In this paper,
we postulate that this comes from the multi-scale structure of musical
information and propose new architectures based on an iterative temporal
aggregation of input labels. Specifically, the input and ground truth labels
are merged into increasingly large temporal bags, on which we train a family of
encoder-decoder networks for each temporal scale. In a second step, we use
these pre-trained encoder bottleneck features at each scale in order to train a
final encoder-decoder network. Furthermore, we rely on different reductions of
the initial chord alphabet into three adapted chord alphabets. We perform
evaluations against several state-of-the-art models and show that our
multi-scale architecture outperforms existing methods in terms of accuracy and
perplexity, while requiring relatively few parameters. We analyze musical
properties of the results, showing the influence of downbeat position within
the analysis window on accuracy, and evaluate errors using a musically-informed
distance metric.
