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This class is usually taught in the summer term. The class is taught in English.
Information for the current semester (if available):
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Prerequisites
The course will start with brief introductions to Musicology and Signal Processing. Prior knowledge in these areas may be helpful but is not a requirement.
Goal
As a participant in this course, you will become familiar with several music audio analysis problems. For each, you will learn about proposed solutions using signal processing, classical algorithms and/or machine learning. A practical project will allow you to experiment with and combine the methods you learned and strengthen your problem solving skills as an individual and teammate. You will be ready to understand and tackle other audio analysis tasks.
Outline
The course is structured along different tasks in the automatic analysis of music audio via computer algorithms. These tasks include:
- Onset Detection
- Beat and Tempo Tracking
- Pitch Tracking
- Chords and Tonality Detection
- Audio Fingerprinting
- Some more complex systems (Music Similarity, Score Following, ...)
Lectures
Lectures take place weekly in Linz. Physical attendance is highly welcome, but purely remote participation is possible. Presentation slides, lecture streams and/or recordings will be made available via Moodle.
Exercise Track
During this course you will implement a prototypical music analysis system, consisting of an Onset Detector, a Beat Tracker and a Tempo Estimator (in Java, Python or a language of choice). All submitted systems will be compared against each other on a shared dataset.
Exam
At the end of the semester, there will be an open-book online exam on the course contents (available to all participants who completed the exercise track). For those who failed or could not attend the exam, individual oral exams will be coordinated with the participants as needed.
