Neural Band-to-Piano Score Arrangement with Stepless Difficulty Control

Abstract

This paper describes a music arrangement method of popular music that can convert a band score into a piano score with a steplessly-specified level of performance difficulty. The basic strategy of band-to-piano score arrangement is to select notes from an augmented band score obtained by up- and down-shifting the notes of an original band score by one octave. Given band scores and the corresponding piano scores with elementary- and advanced-levels, one can train a deep neural network (DNN) that estimates note masks conditioned by the difficulty levels. Conditioned by an intermediate level at runtime, however, the DNN tends to generate an advanced-level score. To solve this problem, assuming that an easier piano score is a subset of harder one, we estimate the basic importance of each note with a difficulty-agnostic DNN and then warp it with a power function depending on a specified difficulty level. To achieve the fine controllability of the difficulty level, we propose a training method that subjects the DNN to generating piano scores with various intermediate levels, where the note-level loss for those scores is evaluated using only the ground-truth elementary- and advanced-level scores. Considering the non-uniqueness of piano arrangement, the statistic-level loss with respect to the note density and polyphony level is also computed according to the given levels. The experimental results showed that the proposed method attained both the performance gain and the stepless difficulty control.