# Word Embeddings for Automatic Equalization in Audio Mixing

arXiv: https://arxiv.org/abs/2202.08898 · HTML: https://mlsanity.com/paper/2202.08898/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [A Survey of Word Embeddings Evaluation Methods](https://mlsanity.com/paper/1801.09536/) (2018)
- [ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge](https://mlsanity.com/paper/1704.03560/) (2017)

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

- [Communicating Sound Through Natural Language](https://mlsanity.com/paper/2605.08750/) (2026)

## Abstract (arXiv)

In recent years, machine learning has been widely adopted to automate the audio mixing process. Automatic mixing systems have been applied to various audio effects such as gain-adjustment, equalization, and reverberation. These systems can be controlled through visual interfaces, providing audio examples, using knobs, and semantic descriptors. Using semantic descriptors or textual information to control these systems is an effective way for artists to communicate their creative goals. In this paper, we explore the novel idea of using word embeddings to represent semantic descriptors. Word embeddings are generally obtained by training neural networks on large corpora of written text. These embeddings serve as the input layer of the neural network to create a translation from words to EQ settings. Using this technique, the machine learning model can also generate EQ settings for semantic descriptors that it has not seen before. We compare the EQ settings of humans with the predictions of the neural network to evaluate the quality of predictions. The results showed that the embedding layer enables the neural network to understand semantic descriptors. We observed that the models with embedding layers perform better than those without embedding layers, but still not as good as human labels.