Master’s Theses, Student Assistantships & Semester Projects

We are always looking for students to collaborate with, routinely supervising Master’s theses at the intersection of musicology and computing. We also offer for-credit semester projects and, occasionally, part-time employment opportunities (internships, assistantships).

  1. If you are inquiring about semester or thesis projects, please read the following project list and contact Dr. Yannis Rammos, or the designated project researcher, laying out your relevant background. You are also welcome to submit your own research proposal to us, in the form of a few sentences.
  2. If you are interested in pursuing a remunerated opportunity (internship or assistantship), please follow the instructions on this page  (unless instructed otherwise in the description).

Music Explorer: Analysis & Visualization of Large Music Corpora (2 positions)

 Paid internship

Our lab recently prototyped MusicExplorer, a web app for analyzing large corpora (datasets) of music and music metadata. Both front- and backend are written in Plotly Dash, a Python-based framework. The web app effectively serves as a frontend to dimcat, our Digital Musicology Corpus Analysis Toolkit.

We are now seeking two (2) student assistants able to build on the preexisting code base by implementing the following new features:

Prerequisites:

  • excellent software engineering practices (code quality, maintainability);
  • Python mastery (preferably with experience in Plotly Dash);
  • expertise or strong interest in REST API development;
  • fluency in Python dataframe manipulation (e.g. Pandas, Modin);
  • adaptability to preexisting code bases;
  • expertise or strong interest in UI/UX design.

Duration & Terms:

Compensated at 25CHF per work-hour (gross), the selected assistants will be available, ideally, by June 2024 (or by September 2024 at the latest). The project expires on December 31, 2024. The positions are subject to the applicable EPFL regulations.

Applying:

As our funding is time-limited, interested students are urged to apply the soonest possible.

To apply, please send us:

  • a current resumé;
  • a brief motivation letter outlining relevant skills and project experience;
  • a current academic transcript.

Extending our music processing library

 Semester project

We are in the process of building up an extensive music processing toolkit by integrating existing frameworks as well as developing new functionality. (Our dimcat library, for example, is part of this effort.) Apart from basic audio processing functionality, the toolkit’s focus is on audio-to-symbolic transcription of music and music processing and analysis on the symbolic level. This includes things like:

  • beat inference
  • measure inference
  • harmonic inference
  • motive analysis
  • integrating or reproducing the state-of-the-art, among others
  • reading/writing/converting between various commonly used formats
  • “rapid prototyping” (integration with visualization and sonification functionality from above)

Contact: Johannes Hentschel

Modeling the note level of jazz harmony

 Master's thesis 

Harmonic analyses in the form of chord annotations represent a coarse-grained segmentation of the score with a given chord label expressing an abstraction of a particular segment. Such abstract chord labels therefore enable researchers to investigate differences and common patterns in different tonal languages. The DCML has access to a small corpus of Jazz transcriptions with included chord labels (changes). The aim of this thesis would be to relate the abstract labels to the concrete notes that the performers played in the respective segments, and to look for significant stylistic differences in various performers’ realization of particular chords.
The project is aimed at a highly motivated student interested in jazz harmony and its realization in particular jazz recordings. It requires music theoretical knowledge and very good skills in programming and data analysis. Prior experience with machine learning is an asset but not a requirement. Methodological choices are to be discussed.

Contact: Johannes Hentschel

Tradition and innovation in the notation of linear analysis

 Master's thesis

Over one century since their inception, linear techniques of music analysis—such as those of Leo Mazel, Heinrich Schenker, and Célestin Deliège—remain among the most insightful instruments available for the interpretation of classical music and related genres. Linear analyses seek “deep structures” within the music score, often using extended score notations which pose challenges to common practices of score reading, music typography, symbolic encoding, and graphic rendering. Our lab has already developed an MEI-based representation of such analyses, a set of encoding guidelines, a web interface for encoding, as well as a corpus of canonical MEI-encoded music analyses (soon to be published). This project has a two-pronged goal: first, to develop a Javascript renderer of MEI-encoded analyses in score notation, building on the Verovio library; second, to design radically innovative graphic representations of these analyses, departing from those of historical theorists, potentially borrowing ideas from network visualization or even cartography.

Prerequisites:

  • interest in digital music typography and creative visualization;
  • expertise in vector graphics techniques (incl. SVG);
  • fluency in mathematical graphs;
  • fluency in music score reading;
  • basic knowledge of Western tonal harmony and principles of polyphony;
  • excellent Javascript/ES6 or Python skills;
  • good writing skills in the English language.

Indicative bibliography:

  • Ericson, P., Rammos, Y., & Rohrmeier, M. (2023). A Generic Framework for Hierarchical Music Analysis. Music Encoding Conference Proceedings. https://doi.org/10.17613/bzaj-qq94
  • Gould, E. (2011). Behind Bars: The Definitive Guide to Music Notation. Alfred Music.
  • Schenker, H. (1969). Five Graphic Analyses. Dover. (Original work published 1932.)

Contact: Yannis Rammos