# Learning and Evaluating Musical Features with Deep Autoencoders

arXiv: https://arxiv.org/abs/1706.04486 · HTML: https://mlsanity.com/paper/1706.04486/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2017.

## Built on by (most cited first, 2 of 2)

- [Signal-domain representation of symbolic music for learning embedding spaces](https://mlsanity.com/paper/2109.03454/) (2021)
- [Emergent Tonal Structure in Learned Chord Embeddings and Its Relation to Tonal Tension](https://mlsanity.com/paper/2609.36460/) (2026)

## Abstract (arXiv)

In this work we describe and evaluate methods to learn musical embeddings. Each embedding is a vector that represents four contiguous beats of music and is derived from a symbolic representation. We consider autoencoding-based methods including denoising autoencoders, and context reconstruction, and evaluate the resulting embeddings on a forward prediction and a classification task.