Jazz Contrafact Detection

TL;DR

This paper introduces a new vector-space model for detecting jazz contrafacts by representing chord progressions and measuring harmonic similarity, effectively handling reharmonizations.

Contribution

It presents a novel harmonic similarity metric and a vector-space approach tailored for contrafact detection in jazz music.

Findings

Successfully applied to 2,612 chord progressions from Impro-Visor corpus

Demonstrates ability to detect contrafacts despite reharmonizations

Provides examples showing effectiveness in real jazz datasets

Abstract

In jazz, a contrafact is a new melody composed over an existing, but often reharmonized chord progression. Because reharmonization can introduce a wide range of variations, detecting contrafacts is a challenging task. This paper develops a novel vector-space model to represent chord progressions, and uses it for contrafact detection. The process applies principles from music theory to reduce the dimensionality of chord space, determine a common key signature representation, and compute a chordal co-occurrence matrix. The rows of the matrix form a basis for the vector space in which chord progressions are represented as piecewise linear functions, and harmonic similarity is evaluated by computing the membrane area, a novel distance metric. To illustrate our method's effectiveness, we apply it to the Impro-Visor corpus of 2,612 chord progressions, and present examples demonstrating its ability to account for reharmonizations and find contrafacts.