Analysis of Improvised Jazz Melodies Using Harmonic Tags

Jazz improvisation has many similarities to spoken language, and it might be expected that large language models would be effective tools for information retrieval and generative applications applied to it. There are, however, important practical differences. The success of modeling natural language has, in part, been due to the availability of vast corpora of symbolic text. By comparison, collections of transcribed jazz are orders of magnitude smaller. For this reason, neural architectures are unlikely to be as effective for music as they have been for text without the support of additional information. For applications with limited data, various strategies have been shown to be helpful, one of which is the injection of domain knowledge. The objective of this paper is to analyze the relationship between melody and harmony as a method for extracting jazz-specific domain knowledge. To that end, we describe an automated system for identifying and tagging harmonic features of jazz melody, and apply it to a corpus of 325 transcribed, bebop-style solos with over 300,000 notes. A unique aspect of our work is that the tags are based on terminology used by jazz musicians, and this allows us to directly analyze the statistical characteristics of improvisational devices taught by educators and found in instructional books. Our analysis confirms the expressiveness of harmonic tagging, and identifies a convergence of vocabulary used across the thirteen musicians represented in our data. The results show that harmonic tags capture useful domain knowledge and should be beneficial in improving the effectiveness and accuracy of deep learning architectures applied to jazz applications.