Visual Analytics of Melodic Patterns

Theoretical (Analytical):

Practical (Implementation):

Literature Work:


  • Analyzing melodic patterns or themes in music is a common musicological task 
  • The automatic idenitification of melody information is a challenge that can benefit from interactive visualization
  • Your target in this project is to develop/extend an interactive visual analysis prototype that supports the visual analysis melodic patterns based on music data that is available in the MusicXML format.
  • There are numerous style means such as repetition, inversion, and transposition that are relevant to musicologists when analyzing melodic features due to the impact on the listener.

Problem Statement

  • How can we use interactive visual methods to identify specific melodic patterns?
  • What aspects are important when implementing an analysis prototype that supports the visual analysis and identification of melodic patterns?


  • Implement given melodic operators that can be applied to a one-dimensional melodic search pattern such as inversion, repetition, transposition, augmentation, diminuition
  • Identify recurring patterns within single musical pieces and extend this approach to a collection of selected musical pieces (dataset will be provided)
  • Implement user interaction techniques that enable music analysts to apply the melodic operators mentioned above
  • Create a visualization that provides an overview of the results of user inquiries or identify frequent melodic patterns. 


  • Interest in learning about music data (e.g., notes, pitches, rhythm).
  • Basic knowledge about information visualization and data mining.
  • Good programming skills in Python (for backend) and web programming skills (HTML / JavaScript (and TS) / D3).
  • Readiness to do creative research work independently

Scope / Duration / Start

  • Scope: Bachelor / Master
  • Project/Thesis Duration: 3 months / 3 months (Bachelor), 3 months / 6 months (Master)
  • Start: Consider the project registration deadlines provided by the Department of Computer and Information Science (BA | MA)



  • M. Good. MusicXML for notation and analysis. The virtual score: representation, retrieval, restoration 12:113-124, 2001.
  • Cuthbert, M. S., & Ariza, C. music21: A toolkit for computer-aided musicology and symbolic music data. (2010).
  • Mikael Laurson, Mika Kuuskankare, Vesa Norilo: An Overview of PWGL, a Visual Programming Environment for Music. Comput. Music. J. 33(1): 19-31 (2009)
  • Jan Koláček. Global Chant Database. (2009)
  • Irwin, K. Musipedia: The open music encyclopedia. Reference Reviews. (2008):
  • Kelkar, T., & Jensenius, A. R. Exploring melody and motion features in “sound-tracings”. In Proceedings of the SMC Conferences (pp. 98-103). Aalto University. (2017).