# SOMson -- Sonification of Multidimensional Data in Kohonen Maps

arXiv: https://arxiv.org/abs/2404.00016 · HTML: https://mlsanity.com/paper/2404.00016/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Computational timbre and tonal system similarity analysis of the music of Northern Myanmar-based Kachin compared to Xinjiang-based Uyghur ethnic groups](https://mlsanity.com/paper/2103.08203/) (2021)
- [Three Orthogonal Dimensions for Psychoacoustic Sonification](https://mlsanity.com/paper/1912.00766/) (2019)
- [Goniometers are a Powerful Acoustic Feature for Music Information Retrieval Tasks](https://mlsanity.com/paper/2302.01090/) (2023)
- [Three-Dimensional Sonification as a Surgical Guidance Tool](https://mlsanity.com/paper/2310.09070/) (2023)
- [Introducing STRAUSS: A flexible sonification Python package](https://mlsanity.com/paper/2311.16847/) (2023)

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

- [Timbre Analysis of the Hulusi, a Southwestern Chinese Free-Reed Instrument, using Machine Learning](https://mlsanity.com/paper/2609.17612/) (2026)

## Abstract (arXiv)

Kohonen Maps, aka. Self-organizing maps (SOMs) are neural networks that visualize a high-dimensional feature space on a low-dimensional map. While SOMs are an excellent tool for data examination and exploration, they inherently cause a loss of detail. Visualizations of the underlying data do not integrate well and, therefore, fail to provide an overall picture. Consequently, we suggest SOMson, an interactive sonification of the underlying data, as a data augmentation technique. The sonification increases the amount of information provided simultaneously by the SOM. Instead of a user study, we present an interactive online example, so readers can explore SOMson themselves. Its strengths, weaknesses, and prospects are discussed.