Every track carries a set of measurable audio features: energy, valence, danceability, acousticness, tempo, and more. On their own those numbers are hard to read. The Sonic Genome treats the whole catalog as data and turns it into something you can actually navigate.
I reduced the high-dimensional feature space down to two dimensions with UMAP so similar-sounding music sits close together, then mapped the same data four ways: the overall landscape, the mood split, the signature of each genre, and how the features move together. Each figure below is interactive. Hover, zoom, and pan.
Figure 1. Every track placed by sound. The high-dimensional feature space is projected to two dimensions with UMAP, so neighbors here are neighbors in sound.
Figure 2. Valence (sad to happy) against energy, sorting tracks into mood quadrants from calm and melancholy to bright and intense.
Figure 3. The average audio-feature profile of each genre, drawn as a fingerprint, so you can see what actually makes one genre sound different from another.
Figure 4. How the audio features move together, as a Pearson correlation matrix. Loud tracks tend to be energetic; acoustic tracks tend not to be.
Dataset
Built from per-track audio-feature data across many genres.
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