Master Thesis Projects

All information for students, EPFL Professors and companies/universities

DH Master Thesis Project Information (PDF)

Presentation for students

DH Master Thesis Presentation Final (PDF)

Form for Master thesis projects outside of EPFL

Form Master Thesis Project Outside EPFL (doc)

Available Master Thesis Projects in DH:

Project Title: List of projects

Professor: Robert West

All projects offered by Prof. West’s lab are listed here

Contact: Prof. Robert West

Project Title: List of projects

Professor: Daniel Gatica-Perez

All projects offered by the Social Computing Group are listed here

Contact: Prof. Daniel Gatica-Perez

Project Title: Analyse des livres rares à la Bibliothèque nationale de France

Professor: Frédéric Kaplan

Project summary: Projet de master ayant comme corpus d’étude un ensemble homogène (en terme de mise en page) d’incunables illustrés numérisés. L’étudiant mettra en place une stratégie de segmentation pour rechercher des ornements typographiques (bandeaux, cul-de-lampe, lettrines, encadrements de pages de titres, etc.) et identifier l’emplacement des gravures sur les pages. Il devra ensuite construire un outil permettant de repérer le remploi de matrices de gravures sur bois (repérage d’images identiques ou proches) et extraire les réseaux liant des ensembles de matrices à certaines productions. Par cette approche la structure du réseau des imprimeurs devrait apparaître. Le stage se fera à Paris en co-supervision avec les responsables de collection.

Contact: Prof. Frédéric Kaplan

Project Title: Analyse des manuscrits enluminés à la Bibliothèque nationale de France

Professor: Frédéric Kaplan

Project summary: Projet de master ayant comme corpus d’étude un ensemble homogène (en terme de mise en page, époque…) de manuscrits enluminés numérisés. L’étudiant mettra en place une stratégie de segmentation sur l’identification de l’emplacement d’une enluminure sur une page. Au sein du corpus d’enluminures, il s’agira d’identifier un contenu iconographique (par ex. un animal) des enluminures pour permettre la recherche mais aussi une analyse quantitative des éléments iconographiques représentés. Le stage se fera à Paris en co-supervision avec les responsables de collection.

Contact: Prof. Frédéric Kaplan

Project Title: Reconstruction du Louvre en 4D

Professor: Frédéric Kaplan

Project summary: L’objectif du projet est d’utiliser les documents d’archives du Louvre pour produire une représentation évolutive multi-échelle de sa structure au travers des siècles. L’étudiant devra développer une approche pour lier les documents numérisés qui documentent le modèle à une plateforme géohistorique. Un des enjeux sera la représentation des incertitudes du modèle de manière visuelle et la possibilité d’effectuer une modélisation incrémentale et versionnée. Le stage se fera à Paris en co-supervision avec la responsable des recherches sur l’histoire du Louvre.

Contact: Prof. Frédéric Kaplan

Project Title: Numérisation des grandes collections photographiques à Venise

Professor: Frédéric Kaplan

Project summary: L’objectif du projet est l’analyse du corpus de plusieurs centaines de milliers de photographies d’art constitué dans le cadre du projet Replica. L’enjeu sera de résoudre par une méthode semi-automatique les problèmes d’attribution concernant plusieurs milliers d’oeuvres en comparant les résultats avec d’autres grandes bases de données numérisées. Le projet aura lieu à Venise à la fondation Cini.

Contact: Prof. Frédéric Kaplan

Project Title: Harmonic Analysis using Voice-Leading Reduction

Professor: Martin Rohrmeier

Project summary: Harmonic analysis of musical pieces works very differently when done automatically than when done manually by experts. Part of the reason is that human experts are able to correctly distinguish chord notes from ornamentation notes. The goal of this thesis is to develop a model for surface ornamentation based on voice-leading operations and to use it for harmonic analysis.

Contact: Christoph Finkensiep

Project Title: Evaluating variability in harmonic annotations of Western classical music

Professor: Martin Rohrmeier

Project summary: Harmonic analysis is a different task for humans as well as computers. In order to improve algorithmic approaches, the Digital and Cognitive Musicology lab is creating large datasets of expert labels for Western classical music (Bach, Mozart, Beethoven, …). Naturally, different annotators might choose different labels in the same situation. This master project aims at investigating the variability in the harmonic annotations which has not yet been done extensively for Western classical music.

Contact: Fabian Moss

Project Title: Visual Hierarchical Analysis of Tonality using the Discrete Fourier Transformation

Professor: Martin Rohrmeier

Project summary: Tonal Music inhabits nested tonal structures on various time scales. For example, chords commonly sound only a short amount of time and are embedded in longer tonal constituents such as keys. While local tonal relations can be captured by deep learning models, these approaches fail to represent the large-scale structures of music. In contrast, hierarchical approaches are computationally expensive to train and execute.

The topic of this master project is to use the spacial representation of tonal relations in the Fourier phase space to analyze and visualize hierarchical structures of tonality. The applicants are expected to be confident in programming and to have at least a basic understanding of music. Prior knowledge about the discrete Fourier transform is an asset but not required.

References:

Yust, J. (2018). Geometric Generalizations of the Tonnetz and Their Relation to Fourier Phases Spaces. In Mathematical Music Theory: Algebraic, Geometric, Combinatorial, Topological and Applied Approaches to Understanding Musical Phenomena, p 253.

Yust, J. (2015). Schubert’s harmonic language and Fourier phase space. Journal of Music Theory, 59(1), 121-181.

Sapp, C. S. (2005). Visual hierarchical key analysis. Computers in Entertainment (CIE), 3(4), 1-19.

Contact: Daniel Harasim

Project Title: Neural Architecture Search with PULSE

Professor: Martin Rohrmeier

Project summary: Despite the huge success of deep learning in various machine learning tasks, designing the network architecture remains a time consuming task to be performed by experts. The need to automatise this task has recently led to advances in neural architecture search (e.g. [2]). A similar endeavour is undertaken for other machine learning models, such as Gaussian processes (e.g. [1]). The PULSE framework [3] was developed to efficiently optimise the structure of machine learning models (e.g. neural networks or Gaussian process models) in large discrete spaces and is suited to unify and improve the approaches that are currently being developed.

The topic of this master project is to reformulate and unify the approaches in [1] and/or [2] in the PULSE framework. Prior knowledge of the PULSE framework is not required but experience with the design of neural networks and/or Gaussian processes is advantageous.

References:

[1] Duvenaud D, Lloyd JR, Grosse R, et al (2013) Structure discovery in nonparametric regression through compositional kernel search. In: Proceedings of the 30th International Conference on International Conference on Machine Learning-Volume 28. pp III–1166

[2] Liu H, Simonyan K, Yang Y (2018) DARTS: Differentiable Architecture Search. arXiv:180609055 [cs, stat]

[3] Lieck R (2018) Learning Structured Models for Active Planning: Beyond the Markov Paradigm Towards Adaptable Abstractions. Phdthesis, Universität Stuttgart

Contact: Robert Lieck

Project Title: Deep Learning for Musical Features

Professor: Martin Rohrmeier

Project summary: Deep learning as shown remarkable performance in extracting relevant features from unstructured data. The goal of this master project is to use deep recurrent neural networks to extract relevant musical features from scores and audio recordings. These features will be used to enhance existing models for the sequential prediction of monophonic [1] and polyphonic music.

References:

[1] Langhabel J, Lieck R, Toussaint M, Rohrmeier M (2017) Feature Discovery for Sequential Prediction of Monophonic Music. In: Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR). Suzhou, China

Contact: Robert Lieck