Learning of Hierarchical Temporal Structures for Guided Improvisation

This article focuses on learning the hierarchical structure of what we call a “temporal scenario” (for instance, a chord progression) to perform automatic improvisation consistently over several different time scales. We first present a way to represent hierarchical structures with a phrase structure grammar. Such a grammar enables us to analyze a scenario at several levels of organization, creating a “multilevel scenario.” We then develop a method to automatically induce this grammar from a corpus, based on sequence selection with mutual information. We applied this method to a corpus of transcribed improvisations based on the chord sequence, also with chord substitutions, from George Gershwin's “I Got Rhythm.” From these we obtained multilevel scenarios similar to the analyses performed by professional musicians. We then present a novel heuristic approach, exploiting the multilevel structure of a scenario to guide the improvisation with anticipatory behavior in an improvisation paradigm driven by a factor oracle. This method ensures consistency of the improvisation with regard to the global form, and it opens up possibilities when playing on chords that do not exist in memory. This system was evaluated by professional improvisers during listening sessions and received excellent feedback.

[1]  Gérard Assayag,et al.  ImproteK: Introducing Scenarios into Human-Computer Music Improvisation , 2017, CIE.

[2]  Maxime Crochemore,et al.  Factor Oracle: A New Structure for Pattern Matching , 1999, SOFSEM.

[3]  Shlomo Dubnov,et al.  OMax brothers: a dynamic yopology of agents for improvization learning , 2006, AMCMM '06.

[4]  Mathieu Giraud,et al.  Computational Fugue Analysis , 2015, Computer Music Journal.

[5]  Sanjit A. Seshia,et al.  Machine Improvisation with Formal Specifications , 2014, ICMC.

[6]  Mark Steedman The Blues and the Abstract Truth: Music and Mental Models , 2009 .

[7]  Robert M. Keller,et al.  Machine Learning of Jazz Grammars , 2010, Computer Music Journal.

[8]  François Pachet,et al.  Assisted Lead Sheet Composition Using FlowComposer , 2016, CP.

[9]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[10]  Robert M. Keller,et al.  A Creative Improvisational Companion Based on Idiomatic Harmonic Bricks , 2012, ICCC.

[11]  Corentin Guichaoua,et al.  Modèles de compression et critères de complexité pour la description et l'inférence de structure musicale. (Compression models and complexity criteria for the description and the inference of music structure) , 2017 .

[12]  François Pachet,et al.  The Continuator: Musical Interaction With Style , 2003, ICMC.

[13]  Emmanuel Vincent,et al.  System & Contrast: A Polymorphous Model of the Inner Organization of Structural Segments within Music Pieces , 2012 .

[14]  Remco C. Veltkamp,et al.  Modeling Harmonic Similarity Using a Generative Grammar of Tonal Harmony , 2009, ISMIR.

[15]  Robert M. Keller,et al.  Automating the Explanation of Jazz Chord Progressions Using Idiomatic Analysis , 2013, Computer Music Journal.

[16]  Emmanuel Vincent,et al.  Probabilistic Factor Oracles for Multidimensional Machine Improvisation , 2018, Computer Music Journal.

[17]  Ran El-Yaniv,et al.  Universal Classification Applied to Musical Sequences , 1998, ICMC.

[18]  Kamel Smaïli,et al.  Beyond the Conventional Statistical Language Models: the Variable-length Sequences Approach , 2022 .

[19]  Matthias Gallé,et al.  Searching for Compact Hierarchical Structures in DNA by means of the Smallest Grammar Problem , 2011 .

[20]  Gérard Assayag,et al.  Generating Equivalent Chord Progressions to Enrich Guided Improvisation : Application to Rhythm Changes , 2017 .

[21]  Nicolas Guiomard-Kagan,et al.  Sketching Sonata Form Structure in Selected Classical String Quartets , 2017, ISMIR.

[22]  Peter Swire,et al.  Learning to Create Jazz Melodies Using Deep Belief Nets , 2010, ICCC.

[23]  Gérard Assayag,et al.  Using Multidimensional Sequences For Improvisation In The OMax Paradigm , 2016 .

[24]  Gérard Assayag,et al.  DYCI2 agents: merging the ”free”, ”reactive”, and ”scenario-based” music generation paradigms , 2017 .

[25]  François Pachet,et al.  Markov constraints: steerable generation of Markov sequences , 2010, Constraints.