Chord Embeddings: Analyzing What They Capture and Their Role for Next Chord Prediction and Artist Attribute Prediction

TL;DR

This paper investigates chord embeddings in music, revealing they encode music-theoretic relationships and improve performance in next chord prediction and artist attribute tasks.

Contribution

It introduces an analysis of chord embeddings, demonstrating their ability to capture music theory and enhance predictive tasks in music analysis.

Findings

Chord embeddings encode music-theoretic relationships.

They improve next chord prediction accuracy.

They benefit musical stylometric tasks.

Abstract

Natural language processing methods have been applied in a variety of music studies, drawing the connection between music and language. In this paper, we expand those approaches by investigating \textit{chord embeddings}, which we apply in two case studies to address two key questions: (1) what musical information do chord embeddings capture?; and (2) how might musical applications benefit from them? In our analysis, we show that they capture similarities between chords that adhere to important relationships described in music theory. In the first case study, we demonstrate that using chord embeddings in a next chord prediction task yields predictions that more closely match those by experienced musicians. In the second case study, we show the potential benefits of using the representations in tasks related to musical stylometrics.