Melody Generation for Pop Music via Word Representation of Musical Properties
Automatic melody generation for pop music has been a long-time aspiration forboth AI researchers and musicians.However, learning to generate euphoniousmelody has turned out to be highly challenging due to a number of factors.Representationof multivariate property of notes has been one of the primary challenges.It is also difficult to remain in the permissible spectrum of musical variety, outsideof 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, aswell as reducing the complexity of learning.We also enforce regularization policieson the range of notes, thus encouraging the generated melody to stay closeto what humans would find easy to follow.Furthermore, we generate melodyconditioned on song part information, thus replicating the overall structure of afull song.Experimental results demonstrate that our model can generate auditorilypleasant songs that are more indistinguishable from human-written ones thanprevious models.
We propose a novel model to represent notes and their properties, which can enhance the automatic melody generation.This paper proposes a generative model of symbolic (MIDI) melody in western popular music which jointly encodes note symbols with timing and duration information to form musical "words".The paper proposes to facilitate generation of melody by representing notes as "words", representing all of the note's properties and thus allowing the generation of musical "sentences".