Made by Dario Baldini.

View the music clustering code on GitHub.




What you see is the 3D map of the vector representation of a music collection.

A pretrained Discogs-EffNet audio model analyzes the track and generate 2560-value embedding for the song. These features summarize aspects of the audio learned by the model; they are not manually assigned genre labels.

The embeddings are standardized and organised by their feature similarity using cosine distance. Dense groups are then found in the high-dimensional semantic space without requiring a preset number of clusters. Songs that do not fit a sufficiently dense group are marked as outliers. A song's cluster probability indicates its membership strength.

Cluster names are descriptive summaries, not genre classifications: they are defined by identifying the most distinctive artists in each cluster.

A separate projection reduces the song embeddings to three dimensions for display. Each point is a song, and nearby points generally have more similar audio embeddings in the high-dimensional semantic space. The projection is a visual aid: distances and directions in 3D are approximate and should not be interpreted as exact measurements.

Navigate the map

Find and explore songs