### PhD 2024-2027 – Modeling and Semi-Automatic Generation of
Musical Arrangements to Foster Ensemble Music Practice
Instrumental pedagogy aims to teach young (and adult) learners
instrumental technique, but equally the joy of playing, alone or
with others, and thus discovering musical repertoire in its
diversity, whether medieval themes, Renaissance, classical and
romantic periods, jazz, pop, world music, etc.
For this purpose, students in their first years of instrumental
study generally play pedagogical arrangements, which are
simplified versions of existing music pieces. Arranging is a
full-fledged professional practice, but many instrumental teachers
have some understanding and create quality arrangements for their
students to help them explore different musical repertoires.
Arranging is particularly practiced for ensembles of players,
whether in music schools, community settings, or in private,
family, or friendly circles. What can a flutist with a few years
of experience, a beginner trumpeter, and a skilled amateur
guitarist play together? Creating or even just selecting suitable
sheet music is often tedious in an amateur setting.
Some publishers or websites offer duets, trios, or other
accessible combinations, but it is rare to find the desired
combinations.
Finding such suitable sheet music serves a musical purpose,
contributes to cultural heritage, and also serves a social purpose
by allowing musicians of different backgrounds to play together.
Would it be possible to have an arrangement of a Handel sarabande
or a Beatles song for two, three, or four players of different
levels? The aim of this thesis is to propose models, algorithms,
and a prototype platform to generate such arrangements taking into
account the diversity of instruments and levels.
One could certainly consider raw or mixed learning approaches,
particularly on conditioned generations. These avenues will be
explored, but we will also focus on how procedural generation,
coupled with learning, could address this issue. We will aim for
high-quality arrangements, created from a meta-arrangement written
by a human arranger. What data structures could represent such a
meta-arrangement, especially with its textures, melodies, and
their variations?
Concretely, the thesis will begin with a state of the art
- in procedural generation,
- in learning and constraint-based generation,
- and notably in conditioning generation methods, by difficulty
as well as instrumentation,
- and in texture and in voice separation and identification.
Then the thesis will propose
- models of a flexible, “instrumentable” musical phrase, in
interaction with arrangers
- the design, implementation, and evaluation of generative model
prototypes coupled with a corpus of meta-arrangements.
This thesis will be in collaboration with arrangers, for example,
with analysis, writing, and orchestration classes at the
conservatories of Lille and Amiens.
The corpora, models, and tools created during this thesis will be
freely distributed. The public deliverable will be a prototype
platform for educational generation, coupled with the Dezrann
platform for musical analysis and sharing, allowing the general
public to experiment with arrangements in various music genres.
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Mathieu Giraud - http://cnrs.magiraud.org/
CNRS, UMR 9189 CRIStAL, Université Lille, Inria, France