Corps / Catégorie
Postdoc/Research Engineer
Lieu
Rennes
Type de contrat
Fixed-term contract: 32 months
Département
Equipe de recherche
Contexte
Collabscore project
Collabscore is a project founded by ANR (French Research National Agency), led by the CNAM. The goal is to study ancient scores provided by the BNF (Bibliothèque National de France) and Royaumont foundation. Collabscore is a multidisciplinary project. The first task consists in improving OMR (Optical Music Recognition) results using learning techniques. The second action will focus on methods for automatic alignment of the scored score with other multimodal sources. The last one will set up demonstrators based on notated scores at two of the project partners, representative, in various ways, of institutions in charge of musical heritage collections (BnF and Fondation Royaumont). Intuidoc team focuses on the first task of musical score recognition.
Collabscore is a project founded by ANR (French Research National Agency), led by the CNAM. The goal is to study ancient scores provided by the BNF (Bibliothèque National de France) and Royaumont foundation. Collabscore is a multidisciplinary project. The first task consists in improving OMR (Optical Music Recognition) results using learning techniques. The second action will focus on methods for automatic alignment of the scored score with other multimodal sources. The last one will set up demonstrators based on notated scores at two of the project partners, representative, in various ways, of institutions in charge of musical heritage collections (BnF and Fondation Royaumont). Intuidoc team focuses on the first task of musical score recognition.
Mission
The post-doctoral/engineer fellow will work on the conception of a OMR system. Based on previous works of our research team, the goal of this position is to enrich an existing system (DMOS-PI) to get a complete OMR system for historical orchestra scores. The tasks are mainly:
- define a grammatical description of musical notation, using the existing DMOS-PI method;
- generate unsupervised data for training musical symbols recognizers;
- create a gradual mechanism for adapting the system to new partitions;
- integrate anomaly detection into the system.
Logical programming from grammars and languages is expected in this work. Machine Learning methods, especially Deep learning-based approaches, will be used to solve some of the tasks, as done in our previous works on music symbol detection.
Pdf version
Profil / Compétences
- PhD, Master degree or Engineering degree in computer science.
- Experience in document recognition or statistical analysis.
- Skills in grammars and languages and/or logical programming are nice-to-have, as well as knowledge of music notation.
- Knowledge in deep learning with an experience with at least one library dedicated to deep learning (Keras, Tensorflow, Pytorch) are expected.
- Experience in document recognition or statistical analysis.
- Skills in grammars and languages and/or logical programming are nice-to-have, as well as knowledge of music notation.
- Knowledge in deep learning with an experience with at least one library dedicated to deep learning (Keras, Tensorflow, Pytorch) are expected.
Durée du contrat (en mois)
32
Quotité
100%
Lieu de travail
Rennes
Diplôme requis
PhD, Master degree or Engineering degree in computer science
Salaire brut mensuel
Up to €36 000 gross annual salary (according to experience), with social security benefits
Date prévisionnelle d'embauche
ASAP
Date limite de candidature
Candidater
Candidates should contact via email: Bertrand Coüasnon (bertrand.couasnon@irisa.fr), Aurélie Lemaitre (aurelie.lemaitre@irisa.fr) and Yann Soullard (yann.soullard@irisa.fr).