A Historical Analysis of Harmonic Progressions using Chord Embeddings

This was a project finished as a team, as Master students at the EPFL in 2021. The outcome was a paper published and presented at the SMC conference in 2021. Here is the abstract:

This study focuses on the exploration of the possibilities arising from the application of an NLP word-embedding method (Word2Vec) to a large corpus of musical chord sequences, spanning multiple musical periods. First, we analyze the clustering of the embedded vectors produced by Word2Vec in order to probe its ability to learn common musical patterns. We then implement an LSTM-based neural network which takes these vectors as input with the goal of predicting a chord given its surrounding context in a chord sequence. We use the variability in prediction accuracy to quantify the stylistic differences among various composers in order to detect idiomatic uses of some chords by some composers. The historical breadth of the corpus used allows us to draw some conclusions about broader patterns of changing chord usage across musical periods from Renaissance to Modernity.