Difficulty-Aware Neural Band-to-Piano Score Arrangement based on Note- and Statistic-Level Criteria

This paper describes a neural music arrangement method that converts a given band score into a piano score with an elementary or advanced level. The major challenge of this task lies in its ill-posed nature, i.e., various piano arrangements are plausible for a band score. In this paper, we take a score reduction approach based on supervised training of a mask estimation network (U-Net) with note- and statistic-level criteria. Based on statistical analysis of existing piano arrangements, a reasonable piano score is assumed to be obtained by reducing an augmented band score obtained by up- and downshifting an original band score by one octave. This effectively narrows down a solution space. At the heart of our approach is to train a U-Net conditioned by a given difficulty level such that a piano score obtained by masking an augmented band score is close to the ground-truth piano score not only at a note level but also at a statistic level. We focus on three kinds of note statistics, i.e., a distribution of the numbers of concurrent notes, that of the intervals between the highest and lowest pitches, and that of the per-measure numbers of notes. The experimental results show the importance of both the instance- and meta-level criteria for supervised training.