Learning Jazz Grammars

We are interested in educational software tools that can generate novel jazz solos in a style representative of a body of performed work, such as solos by a specific artist. Our approach is to provide automated learning of a grammar from a corpus of performances. Use of a grammar is robust, in that it can provide generation of solos over novel chord changes, as well as ones used in the learning process. Automation is desired because manual creation of a grammar in a particular playing style is a labor-intensive, trial-and-error, process. Our approach is based on unsupervised learning of a grammar from a corpus of one or more performances, using a combination of clustering and Markov chains. We first define the basic building blocks for contours of typical jazz solos, which we call “slopes”, then show how these slopes may be incorporated into a grammar wherein the notes are chosen according to tonal categories relevant to jazz playing. We show that melodic contours can be accurately portrayed using slopes learned from a corpus. By reducing turn-around time for grammar creation, our method provides new flexibility for experimentation with improvisational styles. Initial experimental results are reported.