Melody Generation for Pop Music via Word Representation of Musical Properties
This addresses the problem of generating euphonious and structured pop music melodies for AI researchers and musicians, but it is incremental as it builds on existing methods with specific improvements.
The paper tackles automatic melody generation for pop music by representing notes as words and enforcing regularization to maintain musical pleasantness and structure, resulting in generated songs that are more indistinguishable from human-written ones than previous models.
Automatic melody generation for pop music has been a long-time aspiration for both AI researchers and musicians. However, learning to generate euphonious melody has turned out to be highly challenging due to a number of factors. Representation of multivariate property of notes has been one of the primary challenges. It is also difficult to remain in the permissible spectrum of musical variety, outside of which would be perceived as a plain random play without auditory pleasantness. Observing the conventional structure of pop music poses further challenges. In this paper, we propose to represent each note and its properties as a unique `word,' thus lessening the prospect of misalignments between the properties, as well as reducing the complexity of learning. We also enforce regularization policies on the range of notes, thus encouraging the generated melody to stay close to what humans would find easy to follow. Furthermore, we generate melody conditioned on song part information, thus replicating the overall structure of a full song. Experimental results demonstrate that our model can generate auditorily pleasant songs that are more indistinguishable from human-written ones than previous models.