Vertical-Horizontal Structured Attention for Generating Music with Chords
This work provides an incremental improvement in music generation for researchers and practitioners by better capturing chord structures and adhering to music theory.
This paper proposes a lightweight VAE-based model with structured attention for generating music with chords, focusing on the combination of multiple keys within a single instrument. The model effectively captures both temporal and structural relations between keys, outperforming baseline MusicVAE in capturing notes within a chord.
In this paper, we propose a lightweight music-generating model based on variational autoencoder (VAE) with structured attention. Generating music is different from generating text because the melodies with chords give listeners distinguished polyphonic feelings. In a piece of music, a chord consisting of multiple notes comes from either the mixture of multiple instruments or the combination of multiple keys of a single instrument. We focus our study on the latter. Our model captures not only the temporal relations along time but the structure relations between keys. Experimental results show that our model has a better performance than baseline MusicVAE in capturing notes in a chord. Besides, our method accords with music theory since it maintains the configuration of the circle of fifths, distinguishes major and minor keys from interval vectors, and manifests meaningful structures between music phrases.