The lecture introduces the research field of Auditory Data Science which aims at using sound as medium for exploratory data analysis of complex data, for monitoring changes in complex data streams, for discriminating complex time series that are difficult to understand from visualization alone, for supporting exploratory interactions with complex data, to enhance our insight into data by teaming up visualization and sonification towards multimodal data exploration.
Sonification, the systematic, reproducible representation of data as sound is the key to auditory data science and thus sonification techniques will be at the core of this lecture. However, auditory data science has to also focus on established practises for data exploration, involve a basic set of methods for inductive data mining, e.g. for topology learning, intrinsic dimensionality estimation, clustering, covariance estimation. In this lecture we will learn all required techniques, from data mining and sonification to interactive interfaces to realize systems for modern data exploration. A number of real-world examples for auditory data analysis including but not limited to exploration of medical data will be presented and discussed in the lecture. Participants will be capable to use, understand and develop modern sonification systems for understanding structure in high-dimensional data, and be able to craft sound synthesizers and dynamic models for real-time sonification rendering.
The lecture is for anybody interested in scientific applications of auditory display. Helpful, but not mandatory lectures are Sonification & Sound Synthesis, Introduction to Data Mining.
Hermann, Hunt, Neuhoff (Eds.). The Sonification Handbook, Logos Verlag, Berlin, 2011, online at sonification.de/handbook
Frequency | Weekday | Time | Format / Place | Period |
---|
Module | Course | Requirements | |
---|---|---|---|
39-M-Inf-ADS Auditory Data Science | Auditory Data Science | Graded examination
|
Student information |
39-M-Inf-INT-app Applied Interaction Technology | Applied Interaction Technology: Vorlesung | Student information | |
- | Graded examination | Student information | |
39-M-Inf-INT-app-foc Applied Interaction Technology (focus) | Applied Interaction Technology (focus): Vorlesung | Student information | |
- | Graded examination | Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
Degree programme/academic programme | Validity | Variant | Subdivision | Status | Semester | LP | |
---|---|---|---|---|---|---|---|
Studieren ab 50 |
For practical examples and programming we will be using python, numpy, scipy, pandas, matplotlib, pyaudio, and for sonification SuperCollider 3. Prior skills are useful but not mandatory: basic introductions will be given in the lecture and deepened in the exercises.
A corresponding course offer for this course already exists in the e-learning system. Teaching staff can store materials relating to teaching courses there: