Universität Wien

160080 UE Introduction to Python with Applications in Music Information Retrieval and Sound Analysis (2022S)

5.00 ECTS (2.00 SWS), SPL 16 - Musikwissenschaft und Sprachwissenschaft
Continuous assessment of course work
MIXED
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Friday 25.03. 15:00 - 16:30 Digital
  • Saturday 09.04. 09:45 - 13:00 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Saturday 09.04. 14:00 - 17:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Monday 23.05. 09:45 - 11:15 Digital
  • Saturday 11.06. 09:45 - 13:00 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Saturday 11.06. 14:00 - 17:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Friday 24.06. 15:00 - 18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Saturday 25.06. 09:45 - 13:00 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Saturday 25.06. 14:00 - 15:30 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09

Information

Aims, contents and method of the course

This is an introductory course on the Python programming language focusing on applications for music Information Retrieval and sound Analysis. The students will acquire knowledge of basic computer data structures, programming and debugging techniques, open software principles, signal processing with a focus on digital audio, and data visualization techniques. The students will gain practical skills in using Jupyter Notebooks and the version control software Git. Furthermore, they will gain working knowledge of two open-source libraries for sound analysis: the Librosa python package and the Essentia library.

The course is designed to enable students to carry out research projects within the fields of music and audio analysis.

The course format includes lectures, practical courses and exercises in Python. The students will create python scripts to perform specific audio analysis and visualize the results. They will be encouraged to work in group projects using free GitHub accounts for version control.

Assessment and permitted materials

Assessment will be graded based on completion of homework assignments (30%) and a final project (70%).

Minimum requirements and assessment criteria

It is intended for students attending a Master’s or a Bachelor’s program in musicology and requires basic understanding of acoustics. However, it does not require any prior knowledge in computer programming.

Examination topics

Reading list

Müller, M. (2021). Fundamentals of Music Processing Using Python and Jupyter Notebooks. Springer International Publishing. https://doi.org/10.1007/978-3-030-69808-9

McFee, B., Raffel, C., Liang, D., Ellis, D., McVicar, M., Battenberg, E., & Nieto, O. (2015). librosa: Audio and Music Signal Analysis in Python. Proceedings of the 14th Python in Science Conference, Scipy, 18–24. https://doi.org/10.25080/majora-7b98e3ed-003

Steiglitz, K. (1997). A Digital Signal Processing Primer: With Applications to Digital Audio and Computer Music. Addison Wesley Longman Publishing Co. Inc.

Association in the course directory

BA: SYS-V, INT, FRE
MA: M02, M03, M05, M09, M17

EC DH: DH2
MA DH: S-DH Cluster V - Musik

Last modified: Th 11.05.2023 11:27