Subject: [AUDITORY] PhD position in Lille, France, modeling/generation of musical arrangements From: =?utf-8?Q?Florence_Lev=C3=A9?= <=?utf-8?Q?Florence_Lev=C3=A9?=> Date: Tue, 16 Apr 2024 11:02:44 +0200--Apple-Mail=_CFB710D4-D44E-4CD2-9847-576DBBC2C54C Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=utf-8 [Apologies if your receive multiple copies of this message] Dear colleagues,=20 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.=20 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.=20 Applications for this position will be accepted until April 22nd. For further information and other opportunities, please visit = http://algomus.fr/jobs Best regards, Florence Lev=C3=A9 > ### PhD 2024-2027 =E2=80=93 Modeling and Semi-Automatic Generation of = Musical Arrangements to Foster Ensemble Music Practice >=20 > 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. >=20 > 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. >=20 > 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. >=20 > 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? >=20 > Concretely, the thesis will begin with a state of the art >=20 > 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 >=20 > models of a flexible, =E2=80=9Cinstrumentable=E2=80=9D 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. >=20 > --=20 > Mathieu Giraud - http://cnrs.magiraud.org/ > CNRS, UMR 9189 CRIStAL, Universit=C3=A9 Lille, Inria, France --Apple-Mail=_CFB710D4-D44E-4CD2-9847-576DBBC2C54C Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=utf-8 <html><head><meta http-equiv=3D"content-type" content=3D"text/html; = charset=3Dutf-8"></head><body style=3D"overflow-wrap: break-word; = -webkit-nbsp-mode: space; line-break: after-white-space;"><p>[Apologies = if your receive multiple copies of this message]</p><p>Dear = colleagues, </p><div><div><div><p> 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. </p><p>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. </p><p>Applications for this position will = be accepted until April 22nd.</p><p><br> For further information and other opportunities, please visit <a = class=3D"moz-txt-link-freetext" = href=3D"http://algomus.fr/jobs">http://algomus.fr/jobs</a><br> <br> Best regards,<br> <br> Florence Lev=C3=A9</p></div></div><blockquote = type=3D"cite"><div><div><p>### PhD 2024-2027 =E2=80=93 Modeling and = Semi-Automatic Generation of Musical Arrangements to Foster Ensemble Music = Practice</p><p>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.</p><p>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.</p><p>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.</p><p>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?</p><p>Concretely, the thesis will begin with a = state of the art</p> <ul> <li>in procedural generation,</li> <li>in learning and constraint-based generation,</li> <li>and notably in conditioning generation methods, by difficulty as well as instrumentation,</li> <li>and in texture and in voice separation and = identification.</li> </ul><p>Then the thesis will propose</p> <ul> <li>models of a flexible, =E2=80=9Cinstrumentable=E2=80=9D musical = phrase, in interaction with arrangers</li> <li>the design, implementation, and evaluation of generative model prototypes coupled with a corpus of meta-arrangements.</li> </ul><p>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.</p> <pre class=3D"moz-signature" cols=3D"72">--=20 Mathieu Giraud - <a class=3D"moz-txt-link-freetext" = href=3D"http://cnrs.magiraud.org/">http://cnrs.magiraud.org/</a> CNRS, UMR 9189 CRIStAL, Universit=C3=A9 Lille, Inria, France</pre> </div> </div></blockquote></div><br></body></html>= --Apple-Mail=_CFB710D4-D44E-4CD2-9847-576DBBC2C54C--