Universität Wien
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160077 UE Digitisation, Analysis and Visualisation of Audio Data (2025W)

6.00 ECTS (2.00 SWS), SPL 16 - Musikwissenschaft und Sprachwissenschaft
Continuous assessment of course work
Mo 06.10. 16:45-18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09

Details

max. 40 participants
Language: German

Lecturers

Classes (iCal) - next class is marked with N

  • Monday 13.10. 16:45 - 18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Monday 20.10. 16:45 - 18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Monday 27.10. 16:45 - 18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Monday 03.11. 16:45 - 18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Monday 10.11. 16:45 - 18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Monday 17.11. 16:45 - 18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Monday 24.11. 16:45 - 18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Monday 01.12. 16:45 - 18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Monday 15.12. 16:45 - 18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Monday 12.01. 16:45 - 18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Monday 19.01. 16:45 - 18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09
  • Monday 26.01. 16:45 - 18:15 Hörsaal 1 Musikwissenschaft UniCampus Hof 9, 3G-EG-09

Information

Aims, contents and method of the course

Prerequisite:
A laptop or PC running Windows, MacOs or Linux is required to participate in this course and solid general computer skills are a prerequisite. If a laptop is available, you can work along directly during the course.

The course covers 3 areas:

Digitization:
The theoretical part focuses on understanding the digitization process and getting acquainted with the technology needed for it. Here, the conversion of analog data into digital data (AD conversion) is discussed, as well as the technical equipment generally required for recording.

Analysis:
In the practical part, various analysis techniques of digital data are first introduced, which are practiced together in the course units and then applied independently in the analysis tasks. For this purpose, only different open source software with plug-ins will be used (e.g. Audacity, Praat, Sonic Visualiser, ChatGPT, Python etc.).

Visualization:
For a high quality communication of own analysis results in presentations or papers especially for graphical representations all relevant technical parameters (resolution, format etc.) as well as content elements (scaling, labeling ect.) will be discussed.

The exercise is accompanied by a Moodle course where all information and content is provided (slides, sound examples, downloads, links, literature).

Assessment and permitted materials

During the course, exercises will be given, which have to be submitted by the participants via Moodle within a deadline. All information about achievable points and deadlines will also be announced on Moodle. Attendance in the course is also the part of the grade, a maximum of 2 unexcused absences are allowed.

Minimum requirements and assessment criteria

50% of the possible maximum number of points must be achieved in the course to graduate positively. Achievable points and grade levels will be announced via Moodle.

Examination topics

All content discussed in the course, slides, and supplemental material on Moodle will be required for the analysis. An exam will not be held.

Reading list

All materials required for the course will be provided on Moodle.

Association in the course directory

BA: SYS
EC: MAK2

Last modified: Fr 04.07.2025 13:25