Lecture
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5.04.4203 - Angewandte Psychophysik: Anwendungen bei Audioqualitätsbewertungen / Applied Psychophysics: Applications in audio quality
- Stephan Töpken
- Prof. Dr. Steven van de Par
Wednesday: 16:00 - 18:00, weekly (from 15/10/25)
Detailed knowledge of the theoretical concepts underlying listening tests and of modern designs of listening tests. Knowledge about human auditory perception, auditory sensations and their application in e.g. audio quality and digital signal processing.
Basics of auditory physiology and selcted functional models.
Methods for supra-threshold scaling of sounds.
Loudness, selected auditory sensations and timbre.
Spatial hearing and related auditory sensations.
Audio quality ratings and model approaches.
Ethics and data protection.
Seminar talks by students.
Literature:
Pulkki, V., Karjalainen, M. (2015) Communication acoustics : an introduction to speech, audio, and psychoacoustics. Wiley and Sons, ISBN 978-1-118-86654-2
Bech, S., Zacharov, N. (2006) Perceptual audio evaluation: theory, method and application. Wiley and Sons, ISBN 978-0-470-86923-9
Lawless, H.T. (2013) Quantitative sensory analysis. Wiley and Sons, ISBN 978-0-470-67346-1
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Seminar
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5.04.4201 - Oberseminar Kommunikationsakustik
Thursday: 14:00 - 16:00, weekly (from 16/10/25)
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5.04.4204 - Prinzipien der Signalverarbeitung in Hörgeräten
- Prof. Dr. Volker Hohmann, Dipl.-Phys.
- Dr. rer. nat. Giso Grimm
- Dr. rer. nat. Hendrik Kayser
- Kim Marleen Rullmann
Thursday: 10:00 - 12:00, weekly (from 16/10/25)
Understanding the signal processing principles applied to
hearing devices (hearing aids and cochlear implants)
Contents:
- Amplification and compression
- Speech enhancement and noise reduction
- Signal processing in cochlear implants
- Computational auditory scene analysis
- Automatic classification of the acoustic environment
- Acoustic feedback management
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5.04.4213 - Machine Learning I
Wednesday: 10:00 - 12:00, weekly (from 15/10/25)
The field of Machine Learning develops and provides methods for the analysis of data and signals. Typical application domains are computer hearing, computer vision, general pattern recognition and large-scale data analysis (recently often termed "Big Data"). Furthermore, Machine Learning methods serve as models for information processing and learning in humans and animals, and are often considered as part of artificial intelligence approaches.
This course gives an introduction to unsupervised learning methods, i.e., methods that extract knowledge from data without the requirement of explicit knowledge about individual data points. We will introduce a common probabilistic framework for learning and a methodology to derive learning algorithms for different types of tasks. Examples that are derived are algorithms for clustering, classification, component extraction, feature learning, blind source separation and dimensionality reduction. Relations to neural network models and learning in biological systems will be discussed were appropriate.
The course requires some programming skills, preferably in Matlab or Python. Further requirements are typical mathematical / analytical skills that are taught as part of Bachelor degrees in Physics, Mathematics, Statistics, Computer and Engineering Sciences. Course assignments will include analytical tasks and programming task which can be worked out in small groups.
The presented approach to unsupervised learning relies on Bayes' theorem and is therefore sometimes referred to as a Bayesian approach. It has many interesting relations to physics (e.g., statistical physics), statistics and mathematics (analysis, probability theory, stochastic) but the course's content will be developed independently of detailed prior knowledge in these fields.
Weblink: www.uni-oldenburg.de/ml
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5.04.4213 Ü2 - Machine Learning I
- Dmytro Velychko
- Prof. Dr. Bernd Meyer
- Jan Warnken
Tuesday: 16:00 - 18:00, weekly (from 21/10/25), Location: W33 0-003, W02 1-148
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5.04.4213 Ü3 - Machine Learning I
- Prof. Dr. Bernd Meyer
- Jan Warnken
Tuesday: 16:00 - 18:00, weekly (from 21/10/25)
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5.04.4590 - Advanced Topics Speech and Audio Processing
Monday: 16:00 - 18:00, weekly (from 13/10/25), Location: W03 1-154
Thursday: 12:00 - 14:00, weekly (from 16/10/25), Location: W06 0-008
The students will gain in-depth knowledge on the subjects’ speech and audio processing. The practical part of the course mediates insight about important properties of the methods treated in a self-study approach, while the application and transfer of theoretical concepts to practical applications is gained by implementing algorithms on a computer.
content:
After reviewing the basic principles of speech processing and statistical signal processing (adaptive filtering, estimation theory), this course covers techniques and underlying algorithms that are essential in many modern-day speech communication and audio processing systems (e.g. mobile phones, hearing aids, headphones): acoustic echo and feedback cancellation, noise reduction, dereverberation, microphone and loudspeaker array processing, active noise control. During the exercises a typical hands-free speech communication or audio processing system is implemented (in Matlab).
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5.04.663 - Acoustical Metrology and Virtual Acoustics - Akustische Messtechnik
- Prof. Dr. Matthias Blau
- Prof. Dr. Jörg Bitzer
- Prof. Dr. Steven van de Par
- Prof. Dr. Simon Doclo
Monday: 10:00 - 12:00, weekly (from 13/10/25)
Wednesday: 14:00 - 16:00, weekly (from 15/10/25)
Inhalt:
- Nichtlineare und hochauflösende akustische Messverfahren
- Inverse Probleme und Regularisierung
- Akustische Kamera
- Akustische Raumsimulation
- virtuelle Akustik: binaurale Wiedergabe, Lautsprecherwiedergabe (Ambisonics, Wavefield synthesis)
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5.04.813 - Cochlear Implants
- Prof. Dr. Mathias Dietz
- Prof. Dr. Pascale Sandmann
- Rebecca Felsheim
Tuesday: 12:00 - 14:00, weekly (from 14/10/25)
Die Veranstaltung soll ein breites und hinreichendes tiefes theoretisches Fundament legen, um in Wissenschaft und Praxis mit Cochlea Implantaten (CIs) und CI Trägern arbeiten zu können.
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| Prerequisites |
Bachelor in Hörtechnik und Audiologie oder entsprechend |
| Reference text |
Es muss eine Auswahl der folgenden Veranstaltungen im Umfang von insgesamt 6 KP belegt werden. Alternativ können auch Veranstaltungen aus dem Modul „Akustik und Signalverarbeitung II“ belegt werden.
Advanced Topics Speech and Audio Processing, VL/Ü (6 KP)
Angewandte Psychophysik, VL/SE/Ü (3 KP)
Machine Learning I - Probabilistic Unsupervised Learning, VL/Ü (6 KP)
Principles of Signal Processing in Hearing Devices, VL/Ü (3 KP)
Cochlear Implats, VL/SE (3 KP)
Oberseminar Akustik, SE (3 KP)
Lehrform:
Advanced Topics Speech and Audio Processing: Vorlesung: 2 SWS, Übungen: 2 SWS Angewandte Psychophysik: Vorlesung/Seminar/Übungen: 2 SWS
Machine Learning I - Probabilistic Unsupervised Learning: Vorlesung: 2 SWS, Übungen: 2 SWS
Principles of Signal Processing in Hearing Devices, Vorlesung/ Übung: 2 SWS Cochlear Implants, Vorlesung/Seminar: 2 SWS Oberseminar Akusik: Seminar: 2 SWS |
| Module examination |
M |
| Skills to be acquired in this module |
Vermittlung der theoretischen Grundlagen und praktischen Anwendungen moderner Sprachtechnologie. Vermittlung moderner Signalverarbeitungsalgorithmen für digitale Hörgeräte, Cochlear Implantate, Sprachkommunikations- und Audiosysteme. Vermittlung der Grundlagen der Informationsverarbeitung und Informationstheorie, und praktischer Methoden der statistischen Signalverarbeitung, Signalkompression und Nachrichtenübertragung. Messungen akustischer Ereignisse sowie Messungen zur Identifizierung akustischer Systeme. Nach Abschluss des Moduls beherrschen Studierende (a) moderne Signal- und Informations-verarbeitungsmethoden und können (b) die gelernten Methoden zur Analyse schwingungsphysikalischer Systeme und zur Erklärung der Funktionsweise und Analyse signalverarbeitender Systeme einsetzen. |
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