Generating Melodies from Melodic Outlines Towards an Improvisation Support System for Non-musicians

Abstract

One promising approach for supporting non-musicians’ music improvisation is to enable them to input the coarse shape of melodies they want to play. Therefore, drawing a melodic outline is adopted as an input method of existing improvisation support systems. However, generating melodies from melodic outlines with deep neural networks has not been fully exploited. In this paper, we report an attempt to generate melodies from melodic outlines using a convolutional neural network. Given a melodic outline, it is reduced with two convolution layers and then coverted to a sequence of notes with two deconvolution layers. Objective and subjective evaluations imply that our model with sufficient filter width and channels could generate melodies of moderate, close quality to human melodies.