Self-organizing maps (SOMs) as an AI tool for music analysis and production

Self-organizing maps (SOMs) were introduced in the early 1980s by the Finnish researcher Teuvo Kohonen [1], which is why they are also called Kohonen maps. These maps rely on unsupervised learning. Once a system is defined it reorganizes itself without further input, revealing complex patterns and structures.

[Fig: picture of SOM]

This picture shows a SOM. It is often described as a two-dimensional slice through the human brain. Each square represents a single neuron and the colored dots show where specific data is processed. In human brains similar tasks are usually processed in the same region. Similarly, colored dots close to each other on the map refer to similar data.

Each datum that should be used to train such a map must be described as a series of single numbers, the so-called feature vector. When describing music, deriving this vector is not straightforward. Furthermore the vector may be composed of many different values. Thus, it is a high-dimensional vector. All feature vectors used to train the map describe a high-dimensional feature space. A trained Kohonen map provides a mapping from this high-dimensional input layer to a two-dimensional output layer, the so-called unit layer.