phy830 - Acoustics and Signal Processing Part I (Course overview)

phy830 - Acoustics and Signal Processing Part I (Course overview)

Institute of Physics 6 KP
Module components Semester courses Wintersemester 2022/2023 Examination
Lecture
()
Seminar
  • No access 5.04.4201 - Oberseminar Kommunikationsakustik Show lecturers
    • Prof. Dr. Bernd Meyer

    Wednesday: 10:15 - 11:45, weekly (from 19/10/22)

  • No access 5.04.4204 - Prinzipien der Signalverarbeitung in Hörgeräten Show lecturers
    • Prof. Dr. Volker Hohmann, Dipl.-Phys.
    • Dr. rer. nat. Giso Grimm

    Thursday: 10:15 - 11:45, weekly (from 20/10/22)

    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

  • No access 5.04.4213 - Machine Learning I - Probabilistic Unsupervised Learning Show lecturers
    • Prof. Dr. Jörg Lücke

    Wednesday: 12:15 - 13:45, weekly (from 19/10/22), Location: W03 1-156
    Dates on Wednesday, 22.02.2023 15:00 - 19:00, Location: W03 1-161

    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

  • No access 5.04.4213 Ü1 - Machine Learning I - Probabilistic Unsupervised Learning Show lecturers
    • Prof. Dr. Jörg Lücke
    • Till Kahlke

    Tuesday: 16:15 - 17:45, weekly (from 25/10/22)

  • No access 5.04.4213 Ü2 - Machine Learning I - Probabilistic Unsupervised Learning Show lecturers
    • Prof. Dr. Jörg Lücke
    • Filippos Panagiotou

    Tuesday: 16:15 - 17:45, weekly (from 25/10/22)

  • No access 5.04.4213 Ü3 - Machine Learning I - Probabilistic Unsupervised Learning Show lecturers
    • Prof. Dr. Jörg Lücke
    • Dmytro Velychko

    Tuesday: 16:15 - 17:45, weekly (from 25/10/22)

  • No access 5.04.4214 - Advanced Models and Algorithms in Machine Learning Show lecturers
    • Prof. Dr. Jörg Lücke

    Monday: 08:15 - 09:45, weekly (from 17/10/22)

    The students will learn about recent developments and state-of-the-art approaches in Machine Learning, and their applications to different data domains. By presenting scientific studies in the context of currently used models and their applications, they will learn to understand and communicate recent scientific results. The presentations will use computers and projectors. Programming examples and animations will be used to support the interactive component of the presentations. In scientific discussions of the presented and related work, the students will obtain knowledge about current limitations of Machine Learning approaches both on the theoretical side and on the side of their technical and practical realizations. Presentations of interdisciplinary research will enable the students to carry over their Machine Learning knowledge to address questions in other scientific domains. Contents: In this seminar recent developments of models and algorithms in Machine Learning will be studied. Advances of established modelling approaches and new approaches will be presented and discussed along with the applications of different current algorithms to application domains including: auditory and visual signal enhancements, source separation, auditory and visual object learning and recognition, auditory scene analysis and inpainting. Furthermore, Machine Learning approaches as models for neural data processing will be discussed and related to current questions in Computational Neuroscience.

  • No access 5.04.663 - Acoustical Metrology and Virtual Acoustics - Akustische Messtechnik Show lecturers
    • Prof. Dr. Matthias Blau
    • Prof. Dr. Jörg Bitzer
    • Prof. Dr. Steven van de Par

    Monday: 10:15 - 11:45, weekly (from 17/10/22), Location: W03 1-154
    Wednesday: 14:15 - 15:45, weekly (from 19/10/22), Location: W16A 010

    Lernziel: Fähigkeit, Messunsicherheiten entsprechend GUM berücksichtigen zu können Verständnis fortgeschrittener Verfahren der akustischen Messtechnik mit dem Ziel, diese Verfahren bewerten, implementieren und anwenden zu können. Inhalt: Messunsicherheiten – GUM, Schlecht gestellte Probleme – Regularisierung, Zoom-FFT / hochauflösende Verfahren, Messung von Nichtlinearitäten, spezielle Anwendungen (Messung der Schallintensität, in-situ-Messung von Reflektanz und Absorptionsgrad, akustische Kamera, ...)

  • No access 5.04.813 - Cochlear Implants Show lecturers
    • Prof. Dr. Mathias Dietz

    Tuesday: 12:15 - 13:45, weekly (from 18/10/22)

    Die Veranstaltung soll ein breites und hinreichendes tiefes theoretisches Fundament legen, um in Wissenschaft unf Praxis mit Cochlea Implantaten (CIs) und CI Trägern arbeiten zu können.

Exercises
Hinweise zum Modul
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.