2020-02793 - PhD Position F/M Similarity Metrics for Music Notation and Information Retrieval in Digital Music Score Databases
Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD

Niveau de diplôme exigé : Bac + 5 ou équivalent

Fonction : Doctorant

Contexte et atouts du poste

The PhD will take place within Inria's exploratory action Codex (2020-2023), a research project on the processing of written music, in collaboration with the research team Vertigo, "complex data, learning, and representations" in Conservatoire des Arts et Métiers (CNAM), Paris and musicologists from IReMus CNRS research unit, digital library practitioners from French National Library (BnF), and computer scientists from TRS lab at Nagoya University (via a JSPS grant).

Codex addresses problems such as automated music transcription, computational musicology, research and indexing in the collection of digital scores, as well as crowdsourcing approaches for score digitization and edition.

The PhD work location will be the Cédric lab, in CNAM, Paris, France.

Contact information:

  • Florent Jacquemard <florent.jacquemard@inria.fr>
  • Philippe Rigaux <philippe.rigaux@lecnam.net>

Mission confiée

This PhD work will focus on the study of structured models for the representation of music notation, and the design of similarity metrics and indices for digital music score databases complying with these models, with digital musicology as a primary case study.

For more details, you may consult our PhD proposal at:


Principales activités

Music Information Retrieval (MIR) is a multidisciplinary field concerned with the processing, organization, access and analysis of musical content in various formats such as audio recordings, symbolic performance recordings (MIDI), musical scores... Approaches developed in this area rely on different acoustic models and language models. The latter are often based on sequential (1D) or geometric (2D) representations of musical events (notes with attributes of pitch, start time, and duration).

Common Western Music Notation (CWN) is a graphical format used for centuries as a crucial vector for knowledge transmission in musical practice. Although based on a relatively small number of symbols, this format is much more structured and conveys more information than the aforementioned representations. It indeed describes local and non-local relationships and a hierarchical organization of melodic and harmonic content in rhythmic groups, sentences, etc. Such information is useful to musicians for the understanding and interpretation of pieces, and can also be exploited in MIR tasks.

The objective of this PhD is to study

  1. structured music representations sharing fundamental properties with CWN,
  2. language models & formalisms based on such representations and
  3. their application to several MIR tasks.

We shall in particular focus on the two following problems for these models:

  • the construction of index for fast retrieval in digital music score databases,
  • the definition and efficient computation of similarity metrics like edit distances.

Particular attention will be dedicated to the development of a relevant use case in computational musicology, in collaboration with musicologists at Sorbonne University. The development of collections and tools will be integrated into the base Neuma to demonstrate the practical impact and to disseminate the project's results.


We expect a strong profile in Computer Science and Music Information Retrieval. Prior knowledge in music representations (audio or symbolic) would be much appreciated. A real interest for interdisciplinary collaborations is also important.


  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
  • Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training