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Introduction to signal processing in the audio and visual domains
Dozentin/Dozent
Mauricio do Vale Madeira da Costa
Beschreibung
Course Description:
This is a course jointly offered by the music department and the
institute of Cognitive Science. The course is open to advanced
bachelor students and master students from both departments.
This course introduces the fundamentals of signal processing,
emphasizing audio and visual applications. The course explores both
theoretical concepts and practical applications. It begins by
introducing audio and visual signals and their representations.
Subsequent blocks will delve into topics such as sampling theory,
digital filters, harmonic transformations, and time-frequency
representations, along with selected applications like codecs and
compression.
On the practical side, we will show how standard Python libraries like
numpy, librosa, and scikit-image are used for signal processing.
Learning Objectives:
By the end of this course, students will be able to:
1. understand the fundamental properties of audio and visual signals,
including their physical nature and characteristics
2. analyze and manipulate audio and visual signals using
appropriate mathematical representations
3. apply basic signal processing techniques, such as sampling,
filtering, and transformations, to solve practical problems
4. utilize common Python libraries for signal processing tasks,
including data analysis and visualization
Prerequisites:
There are no strict prerequisites for this course. The course will
have a certain inclination towards a mathematical treatment and we
expect some openness by the participants to follow along. We do not
expect any specific prior knowledge (though it will not harm) and we
will provide all necessary concepts in class. On the practical side,
we do not expect any coding skills, and will also introduce the
necessary tools and their usage during the sessions.
Course Format:
* Delivery Method: in-person
* Type of Contact & Contact Hours: weekly sessions
* Selection Process: we do not plan any restrictions
Assessment and Grading:
The grades will be based on active participation in class, including
presentation of seminars (30%) and on written exams (70%). We plan
to have five short written exams (30 mins each), and will use the best
four out of five to determine the grade.
Important Dates:
* join the first session if you intend to join this class
* we expect regular attendance in class
* take part in the written exams
Required Texts and Materials or further Resources:
Lectures:
https://signal-processing.pages.gwdg.de/lecture2026
Background material:
* Fundamentals of Music Processing, (Meinard Müller), 2021.
https://link.springer.com/book/10.1007/978-3-030-69808-9
* Foundations of Computer Vision, (Torralba, Isola & Freeman), 2024.
https://visionbook.mit.edu/
https://github.com/Foundations-of-Computer-Vision/visionbook
Will this class be offered again/regularly?
Unsure. The course may be followed up by an advanced course in the
winter term focusing on signal processing in deep neural networks.
| Semester | SoSe 2026 |
| Raum und Zeit | 11/211 |
| Veranstaltungsart | Vorlesung und Übung |
| ECTS | 4 (Cogn.Sc.), 2/3 (Music) |
