
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMusic and Audio Processing · Music Technology and Sound Studies · Neuroscience and Music Perception
