# Conditional LSTM-GAN for Melody Generation from Lyrics

## TL;DR

This paper introduces a new deep generative model, Conditional LSTM-GAN, for melody creation from lyrics, supported by a large aligned lyrics-melody dataset, demonstrating effective and tuneful melody generation.

## Contribution

It presents a novel lyrics-conditioned LSTM-GAN model and a large dataset for improved melody generation from lyrics.

## Key findings

- Generated melodies are plausible and tuneful.
- The model effectively learns lyrics-melody alignment.
- The dataset facilitates future research in lyrics-to-melody synthesis.

## Abstract

Melody generation from lyrics has been a challenging research issue in the field of artificial intelligence and music, which enables to learn and discover latent relationship between interesting lyrics and accompanying melody. Unfortunately, the limited availability of paired lyrics-melody dataset with alignment information has hindered the research progress. To address this problem, we create a large dataset consisting of 12,197 MIDI songs each with paired lyrics and melody alignment through leveraging different music sources where alignment relationship between syllables and music attributes is extracted. Most importantly, we propose a novel deep generative model, conditional Long Short-Term Memory - Generative Adversarial Network (LSTM-GAN) for melody generation from lyrics, which contains a deep LSTM generator and a deep LSTM discriminator both conditioned on lyrics. In particular, lyrics-conditioned melody and alignment relationship between syllables of given lyrics and notes of predicted melody are generated simultaneously. Experimental results have proved the effectiveness of our proposed lyrics-to-melody generative model, where plausible and tuneful sequences can be inferred from lyrics.