Dr.-Ing. Stefan Balke

Data Scientist. Team Leader. Educator. Researcher. Musician.


Höxter, Germany

Trying to be a generalist with a deep understanding and intuition of recent technologies related to Data Engineer, Data Science, and Machine Learning. Currently leading a team of Data Scientists helping customers sorting their data, lifting its quality to ML-readiness, and eventually obtaining business value from modern data-driven approaches.

Before that, I obtained my PhD in applied Machine Learning research, namely Music Information Retrieval, supervised by Prof. Dr. Meinard Müller at the International Audio Laboratories Erlangen. I continued my research as a Post-Doc at the Institute for Computational Perception at JKU Linz, led by Prof. Dr. Gerhard Widmer.

A printable form of my curriculum vitae can be found here.


Sep 2, 2022 Secured another funding by the “Deutsche Gesellschaft für Engagement & Ehrenamt” for financing a PA for the orchestra I am conducting. We will use the PA system to give new impulses during rehearsals (e.g., listening to recordings) as well as using the system at our concerts (e.g., support mic-ing a soloist or an electronic piano). At some point it will pay back to research…stay tuned!
Mar 11, 2022 My proposal for a small funding for music education at my orchestra got accepted by Kreis Höxter. We will use the money to foster our relationships with the local music schools and help educate the next generation of musicians!
Jan 21, 2022 Hooray 🥳, second year in a row I got a “Best Faculty Award” for my teaching obligations at Controller Institute (category >4 days, 3. place).

selected publications

  1. MTD: A Multimodal Dataset of Musical Themes for MIR Research
    Transactions of the International Society for Music Information Retrieval (TISMIR), 3(1), 180–192, 2020
  2. Score Following as a Multi-Modal Reinforcement Learning Problem
    Transactions of the International Society for Music Information Retrieval (TISMIR), 2(1), 67–81, 2019
  3. Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies
    IEEE Signal Processing Magazine, 36(1), 52–62, 2019