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).
- 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.
- If you are interested in pursuing a remunerated opportunity (internship or assistantship), please follow the instructions on this page instead.
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
- matlab midi toolbox
- music21 python framework
- Essentia
- 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 are still among the most revelatory and expressively powerful instruments available for the interpretation of classical music. Linear analyses—based on the techniques of Leo Mazel, Heinrich Schenker, or Célestin Deliège, among several others—capture “deep structures” of a musical work, often using extended score notations which pose challenges to common techniques of symbolic music encoding and score rendering. Our lab has already developed an XML-based representation of such analyses, an interface for analysis encoding, encoding guidelines, as well as a corpus of canonical analyses (soon to be published). This project has a two-pronged goal: first, to develop a Javascript renderer of XML-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 score reading
- basic knowledge of Western tonal harmony and principles of polyphony
- excellent Javascript/ES6 or Python skills
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