DJs and producers of electronic dance music (EDM) often use the term "fat" or "fatness" to describe the perception of different music tracks. However, even though they usually cannot give a sufficient definition of these expressions, professionals in this field of music seem to agree on the meaning of fatness, and increasing the fatness seems to be desirable. For this reason Lars Schmedeke investigated this topic during his Ph.D. [6].

He asked professional DJs and producers of EDM to judge the fatness of several different tracks ranging from the 1960s to 2020. The results were used to derive a mathematical expression from already-known musical parameters. Thus, as it has already been proven that fatness is a relevant feature to describe EDM [6], Schemedeke used it and other psychoacoustic features to train a SOM capable of distinguishing different genres of EDM. The results can be explored in an [to do] interactive online demo.

The chart on the right shows the results of the experts after judging the fatness of different EDM tracks. As fatness is supposed to be desired for modern EDM, it is no surprise that it has increased during the last few decades. On the left is shown how the trained SOM organizes the tracks and, when looking at the associated component plane, how the calculated fatness is distributed over the map. As both charts rely on the same EDM tracks, the SOM can be directly compared with the experts' opinions. One can decide with whom to agree more, the experts or the AI, or maybe both are wrong and one has a completely different opinion. There have always been many answers to such subjective questions.

Observing the SOM reveals that the tracks are also clustered by their colors, which refer to different EDM genres. There are small exceptions, however. These are pieces of music which were often a little bit ahead of their time and sometimes introduced new genres. As a result, they were already at the edge of new musical genres.

Besides musical analysis such a map can also be used in more practical ways, for instance, when organizing personal music libraries. Adding songs to a playlist that are close to each other on the map will result in a collection of songs that are musically similar, rather than relying on arbitrary tags of genre and sub-genre concepts.

Such a SOM may also be a great tool for DJs. Designing a DJ set which should last for the entire night requires, on the one hand, songs rich in variety while, on the other hand, avoiding sudden transitions. One just has to move from one point to another on a SOM because similar music has been arranged close to each other. Sudden transitions can be avoided when the distance between consecutive points on the map is sufficiently small. If the direction of travel is not changed too often one can move, point after point, from one region of the map to another, providing overall a musically diverse DJ set.

Similar techniques can be used if someone in the audience requests a specific track. The DJ may be willing to fulfill those wishes but, often, this would interrupt the musical flow. Using a SOM, the DJ could find a path on the map that prevents sudden musical changes and finally leads to the requested track. With this in mind, everyone is warmly invited to use the online demo to create unique and astonishing DJ sets.