Seminar: Deep learning for audio processing - Details

General information

Course name	Seminar: Deep learning for audio processing
Subtitle
Course number	8.3560
Semester	WiSe 2022/23
Current number of participants	40
Home institute	LE Cognitive Science
Courses type	Seminar in category Offizielle Lehrveranstaltungen
First date	Tuesday, 25.10.2022 10:00 - 12:00, Room: 50/E07
Type/Form
Art der Durchführung	Präsenz-Sitzungen ohne Video-Aufzeichnung [präsenz]
SWS	2
Sprache	Englisch
ECTS points	4

Lecturers

• Ulf Krumnack, Ph.D.
• Axel Schaffland, M. Sc.

Rooms and times

50/E07

Tuesday: 10:00 - 12:00, weekly (13x)

No room preference

Tuesday: 10:00 - 12:00, weekly(1x)

Fields of study

• Veranstaltungen > Cognitive Science > Master-Programm
• Veranstaltungen > Cognitive Science > Bachelor-Programm

Module assignments

• Bachelor of Science Informatik > CS-BWP-NI - Neuroinformatics, valid from WiSe 2019/20
• Bachelor of Science Mathematik > CS-BWP-NI - Neuroinformatics, valid from WiSe 2019/20
• Bachelor of Science Cognitive Science > CS-BWP-NI - Neuroinformatics, valid from WiSe 2019/20
• Master of Science Mathematik > CS-BWP-NI - Neuroinformatics, valid from WiSe 2019/20

Comment/Description

Recent years have seen some spectacular results in the field of sound and audio processing based on deep neural networks, including but not limited to common applications like speech recognition and generation, speaker recogntion, music analysis, noise filtering, and audio compression. In many cases, techniques developed in the context of computer vision can be applied or transferred to the audio domain. This success of deep learning approaches has attracted even more research activities, making it a highly vivid field.

This seminar will introduce basic ideas of processing audio data with deep neural networks and then focus on selected topics, like filtering, analysis and synthesis, in different domains, including speech, music, urban and animal sounds. The course will cover theoretical approaches as well as practical experiments with existing systems. Participants are assumed to have some basic experience in working with deep neural networks.

For COSMOS students not in Osnabrück: in case you would like to participate in this course, please contact us before semester start by email to check if we can find a way to make this work.