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Institute of Computational Perception
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Special Topics: Machine Learning and Audio: a challenge

This class is taught sporadically in either the summer or winter term. The class is taught in English.

Information for the current semester (if available):

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Enrollment in this Course
This is a course targeted at motivated students who like to take on difficult challenges. Together with the lecturers, you will participate in a real-life machine learning challenge, which also includes the publication of a paper.

If you would like to participate in this course, we ask you to send us a short "letter of motivation" (not more than one page). In this letter, please provide us with information on related courses on machine learning and audio processing you have already taken, your experience in machine learning, as well as your personal motivation for choosing this course. Based on the letters, we will select at most 10 students and enroll them in this course. Please send your letters via email to paul.primus@jku.at.

This is a very demanding, but also a very enjoyable course, which can provide you with valuable insights into applied machine learning.


This course will be taught in English. (German is possible, if there are only native speakers)


This course requires (extensive) prior experience with machine learning and familiarity with deep learning frameworks such as Theano, Tensorflow and PyTorch. Programming experience in Python is therefore also a non-negotiable requirement. Furthermore, code will be versioned and shared via GitLab, hence a familiarity with Git and GitLab is a big plus.

Course Outline

Depending on the number of participants, we will form one or more teams. We will choose to either tackle a challenge related to audio and music on a platform such as Kaggle, opens an external URL in a new window, or work on one or more MIREX, opens an external URL in a new window tasks that we collectively deem interesting. Each group will implement a complete data processing pipeline from raw data and annotations to final predictions and including evaluation. Ideally this pipeline will include methods to facilitate the tuning of hyper parameters and model selection. We will of course assist you during this whole process with suggestions and helpful tips on methods and general approaches to such projects. We will provide datasets if we can, or help finding and distributing them, and will also be able to provide a limited amount of (shared!) computing resources.


We will have weekly meetings where you will give a small report (5 slides at most) on your progress so far. Presenters will be selected from each group in a round-robin fashion. Sporadically, we may also review code, and ask individual group members clarification questions. Each group will need to produce a final, written report, detailing their approaches and lessons learnt. The challenges will be done in teams and although the final outcome is not that important, winning one or all of them would be a big plus (for you, your team, and the university). You will be graded individually. The grade is based on attendance, presentations, questions answered, and contributions to code and final report.