A post on the Google Research blog today announced
the open-sourcing of Embedding Projector, a web application for interactive visualization and analysis of high-dimensional data
Over the summer, I presented work (co-authored with Jaan Altosaar) on the application of word embedding models to a large–and admittedly noisy–corpus of classical music. It appears that word embedding models capture something about sequences of chords. Our work, and related recent work with a different dataset, shows that that major triads are somewhat evenly distributed through the embedding space, in their circle-of-fifths order.
There’s no obvious way–yet–to evaluate these models of chords. The Embedding Projector provides a convenient way to inspect the structure of the learned embedding space, and facilitates the exploration of its structure.
You can find a link to an Embedding Projector visualization of the results of an embedding model trained on a small dataset of classical music here.
Switch the labels to “root_common” for more sensible chord labels. Try searching for
(major|minor) triad with regexps enabled in the sidebar.
- Chord slices from the Yale-Classical Archives Corpus…
- from works by Bach, Hadyn, Mozart, and Handel…
- represented as binary chroma vectors (tells us the name of sounding pitch classes)…
- ingested by
gensimas a collection of tokens…
- to train a word2vec model (new dimensionality = 100; context window = 5; skip-gram model; negative sampling used)