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

After an introduction to the mathematical fundamentals of SOMs several examples were discussed. In the field of music and musicology, Kohonen maps are used to analyze music and explain specific effects. They are an excellent tool for gaining deeper insight and answering difficult questions in musicology. They also have to be handled carefully, as the right choice of training data and features will dramatically influence the result, e.g., when organizing [to do] words.

It was also shown that SOMs are helpful when producing music. They can be used to organize sounds, e.g., recordings of [to do] historical pianos and harpsicords. Furthermore, entire musical pieces can be sorted for different musical purposes, e.g., to create unique [to do] DJ sets. A lot of the creative potential of SOMs has yet to be explored. It was roughly touched on when dealing with [to do] syntax, but this can be extended further by rethinking or misusing Kohonen maps. This is not straightforward and the fundamental theoretical and mathematical principles of SOMs must be understood well before they can be manipulated. This, certainly, is one reason why a closer look at mathematics could not be avoided in this talk.

In going beyond the theoretical observation of SOMs, however, this article discusses and links to many interactive examples, and everyone should be encouraged to try them. This is usually a much more intuitive way to get an impression of the benefits of Kohonen maps. How do the maps react? What are the differences between two items arranged at different locations on the map? It is, of course, never wrong to be skeptical. If artificial intelligence detects differences, you can check if you perceive them the same way. And if not, why not? In these cases it is often helpful to have a closer look at the component planes. A new perspective can be obtained in this fashion, even on already-known music. It may be surprising or educational, but it is usually a good moment.

Analyzing SOMs visually using the u-matrix and the component planes, however, is not intuitive. Therefore, in the final example, the advantages of interpreting the maps acoustically were discussed. Even though artificial intelligence is a great tool for analyzing or describing music, the other way around is also true: music and sound can provide a deeper understanding of the results of artificial intelligence and the underlying algorithms.

Now that you have learned the basics of SOMs and have gained first-hand experience in using them, by exploring the provided links, you can make your own opinion about Kohonen maps. Are they a useful tool, or are they something you would not rely on and would rather stick to your own creative mind? Do not forget that if SOMs are trained knowledgeably and responsibly, they may provide an objective view on the emotional topic of music, which may be very helpful, at least in double-checking one's own opinions critically.