Student Assistant Positions

We are always looking for student assistants to collaborate with us.

Visualization/Sonification

  • interactive/live visualization and sonification of notes/music in the browser
  • interactive/live visualization of trees and graphs in the browser
  • interactive/live visualization of various patterns and structures relevant to music in the browser

Extending our music processing library 

We are in the process of building up an extensive music processing library by integrating existing frameworks as well as developing new functionality. Apart from basic audio processing functionality the library’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)

Integration of music equipment 

  • We have different music equipment that has to be integrated with our music processing library. Among others our large digital/acoustic piano, a set of ROLI Lightpads, and a ROLI Seaboard.

Setting up a live framework 

  • There is a number of different tools that can be used for live music performances from acoustic and digital instruments to live-coding environments (sonic pi/super collider/common music/…) to algorithmic tools and autonomous artificial “musicians”. We need to set up a framework that integrates all these tools such that they can interact with each other and share a common “musical space”.

Developing an interface to create Schenker graphs 

  • There are different options to realize this (from a LaTeX package or a browser-based solution to a Musescore plug-in) and part of the project is to explore these alternatives and find out which one best suits our needs.

Redesigning our website

  • This will involve some creativity in coming up with good designs and incorporating input/suggestions from the group as well as the technical realization thereof.

Crawling/digitalizing/transcribing music data

  • crawling the web for music data (scores, midi files, …) from different genres (classical music, jazz, pop, rock, …)
  • digitalizing/transcribing existing data (scanning of scores, transcribing scores by typing them into Musescore)

Conducting behavioural experiments

  • We have a number of behavioural experiment readily designed that need to be carried out. That is, participants need to be recruited, brought to the lab, and the data has to be recorded.

Neural Networks

  • learning voice-leading rules using feed-forward networks (simple) or recurrent neural networks (more advanced)
  • modelling the evolution of language and music via communication using autoencoders