Vertical-Horizontal Structured Attention for Generating Music with Chords

Abstract

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.

References in corpus (8)

• Auto-Encoding Variational Bayes
• A Recurrent Latent Variable Model for Sequential Data
• Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
• SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
• Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
• MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
• Structured Attention Networks
• LakhNES: Improving multi-instrumental music generation with cross-domain pre-training

Cited by in corpus (2)

• The Ethical Implications of Generative Audio Models: A Systematic Literature Review
• SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues