Abstract
Piano reduction is a sheet music that is arranged for piano performance while retaining as much information as possible on a song composed of multiple parts, such as an orchestra. There have been several studies on automatic generation of piano reductions. However, they have had difficulties in dealing with a wide range of genres because they are generated according to predefined rules. Therefore, in this study, we propose a method for automatic generation of piano reduction with enhanced versatility using deep learning. In this system, a CNN-based supervised learning model is used to generate a piano-playable score from a song consisting of multiple parts. We also proposed an algorithm to discriminate the playability of a sheet on the piano and tried to improve the accuracy of the piano reduction. As a result of the evaluation of the generated piano reduction, it was found that the playability was improved.
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