[ 21. April 2024 ]

DEGEM News – FWD – [ak-discourse] PhD position in Lille, France, modeling/generation of musical arrangements

Von: Lepa, Steffen via ak discourse
Datum: Mon, 15 Apr 2024
Betreff: [ak-discourse] PhD position in Lille, France, modeling/generation of musical arrangements

Dear colleagues, Dear community,

The Algomus team, situated in Lille, France (between Paris, London, and Brussels), is pleased to announce a new opportunity for PhD funding focused on the modeling and the generation of music arrangements. We are seeking a student holding a Master’s degree in computer/music science, with expertise in algorithms, data science, and generative models, along with a solid foundation in music theory. Applications for this position will be accepted until April 22nd.

Additionally, we would like to remind you that another PhD opportunity previously advertised, centered on co-creativity and machine learning, remains open for applications.

For further information, please visit http://algomus.fr/jobs

Best regards,


### 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.

Mathieu Giraud – http://cnrs.magiraud.org/

CNRS, UMR 9189 CRIStAL, Université Lille, Inria, France