Audio Engineering Society Convention Express Paper Presented at the 155 th Convention 2023 October 25-27, New York, USA This Express Paper was selected on the basis of a submitted synopsis that has been peer-reviewed by at least two qualified anonymous reviewers. The complete manuscript was not peer reviewed. This Express Paper has been reproduced from the author’s advance manuscript without editing, corrections or consideration by the Review Board. The AES takes no responsibility for the contents. This paper is available in the AES E-Library (http://www.aes.org/e-lib) all rights reserved. Reproduction of this paper, or any portion thereof, is not permitted without direct permission from the Journal of the Audio Engineering Society. Jazz Mapping: An Advanced Framework for Solo Analysis and Discovery in Jazz Music Antonia Petrogianni 1 , Dimitrios Vassilakis 2 , Iraklis A. Klampanos 1 , Theodoros Giannakopoulos 1 , and Areti Andreopoulou 2 1 National Centre for Scientific Research “Demokritos”, Greece 2 National and Kapodistrian University of Athens, Greece Correspondence should be addressed to Antonia Petrogianni (a.petrogianni@iit.demokritos.gr) ABSTRACT Jazz Mapping offers a systematic framework for analyzing jazz improvisation techniques. This method classifies and structures solo components hierarchically based on their musical relevance, providing insights into the distinct musical lexicon utilized by various jazz artists and genres. Implemented for ease of use, our system provides an API interface backed by a PostgreSQL database that contains annotated data for each solo. This allows users to procure basic metrics and source solos from designated artists or eras. Further, our system facilitates detailed analysis of solos by highlighting inter-referential components within the performance, elucidating the structural and thematic nuances of jazz improvisation. 1 Introduction In this work we present a reference implementation of Jazz Mapping [1], an innovative and systematic approach to exploring jazz improvisation by categorizing and segmenting jazz solo components hierarchically based on their significance in improvisation. We also introduce a comprehensive framework and its associated API designed to enrich the understanding of jazz improvisation. The application of syntactic and symbolic analysis according to the “Jazz Mapping” method has given rise to an innovative jazz database. The creation of this database serves three specific purposes. Firstly, to assist in the pedagogy of jazz by gathering a large number of phrases, mannerisms, and musical ideas of the great jazz players. These will serve as examples of style and language to aid aspiring jazz improvisers in their development. Secondly, it seeks to encourage exploration, and experimentation using AI and generative models for jazz improvisation. Lastly, it aims to verify connections between the aspects of jazz improvisation and similar mechanisms used in language production. The development of the database, with the incorporation of annotations as well as structural, semantic, and syntax analyses is driven by the goal to reveal the storytelling [2] qualities of jazz from the standpoint of the jazz player. Additionally, another motivation lies in the quest to understand the