A Historical Analysis of Harmonic Progressions Using Chord Embeddings

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 analyse 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.

Publication
Proceedings of the 18th Sound and Music Computing Conference