# A framework to compare music generative models using automatic evaluation metrics extended to rhythm

arXiv: https://arxiv.org/abs/2101.07669 · HTML: https://mlsanity.com/paper/2101.07669/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 19 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Optical Music Recognition with Convolutional Sequence-to-Sequence Models](https://mlsanity.com/paper/1707.04877/) (2017)
- [Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music](https://mlsanity.com/paper/2012.01231/) (2020)
- [Embeddings as representation for symbolic music](https://mlsanity.com/paper/2005.09406/) (2020)
- [Cross entropy as objective function for music generative models](https://mlsanity.com/paper/2006.02217/) (2020)

## Built on by (most cited first, 1 of 1)

- [Survey on the Evaluation of Generative Models in Music](https://mlsanity.com/paper/2506.05104/) (2025)

## Abstract (arXiv)

To train a machine learning model is necessary to take numerous decisions about many options for each process involved, in the field of sequence generation and more specifically of music composition, the nature of the problem helps to narrow the options but at the same time, some other options appear for specific challenges. This paper takes the framework proposed in a previous research that did not consider rhythm to make a series of design decisions, then, rhythm support is added to evaluate the performance of two RNN memory cells in the creation of monophonic music. The model considers the handling of music transposition and the framework evaluates the quality of the generated pieces using automatic quantitative metrics based on geometry which have rhythm support added as well.