# dMelodies: A Music Dataset for Disentanglement Learning

arXiv: https://arxiv.org/abs/2007.15067 · HTML: https://mlsanity.com/paper/2007.15067/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022)
- [Is Disentanglement enough? On Latent Representations for Controllable Music Generation](https://mlsanity.com/paper/2108.01450/) (2021)
- [Towards Robust Unsupervised Disentanglement of Sequential Data -- A Case Study Using Music Audio](https://mlsanity.com/paper/2205.05871/) (2022)
- [MeloForm: Generating Melody with Musical Form based on Expert Systems and Neural Networks](https://mlsanity.com/paper/2208.14345/) (2022)
- [Disentanglement Beyond Static vs. Dynamic: A Benchmark and Evaluation Framework for Multi-Factor Sequential Representations](https://mlsanity.com/paper/2510.17313/) (2025)
- [MIDI-Draw: Sketching to Control Melody Generation](https://mlsanity.com/paper/2305.11605/) (2023)

## Abstract (arXiv)

Representation learning focused on disentangling the underlying factors of variation in given data has become an important area of research in machine learning. However, most of the studies in this area have relied on datasets from the computer vision domain and thus, have not been readily extended to music. In this paper, we present a new symbolic music dataset that will help researchers working on disentanglement problems demonstrate the efficacy of their algorithms on diverse domains. This will also provide a means for evaluating algorithms specifically designed for music. To this end, we create a dataset comprising of 2-bar monophonic melodies where each melody is the result of a unique combination of nine latent factors that span ordinal, categorical, and binary types. The dataset is large enough (approx. 1.3 million data points) to train and test deep networks for disentanglement learning. In addition, we present benchmarking experiments using popular unsupervised disentanglement algorithms on this dataset and compare the results with those obtained on an image-based dataset.