phy830 - Akustik und Signalverarbeitung Teil I

phy830 - Akustik und Signalverarbeitung Teil I

Institut für Physik 6 KP
Modulteile Semesterveranstaltungen Wintersemester 2021/2022 Prüfungsleistung
Vorlesung
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Seminar
  • Kein Zugang 5.04.4203 - Angewandte Psychophysik: Anwendungen bei Audioqualitätsbewertungen / Applied Psychophysics: Applications in audio quality Lehrende anzeigen
    • Prof. Dr. Steven van de Par
    • Stephan Töpken

    Mittwoch: 16:15 - 17:45, wöchentlich (ab 20.10.2021)

    Detailed knowledge of the theoretical concepts underlying listening tests and of modern designs of listening tests. Knowledge about human auditory perception and its application in e.g. audio quality and digital signal processing. Subjective listening experiment design and models of human auditory perception will be treated with a focus on application in audio quality assessments (e.g. for sound reproduction) and in digital signal processing algorithm development (e.g. for low bit-rate audio coding and headphone virtualizers).

  • Kein Zugang 5.04.4204 - Prinzipien der Signalverarbeitung in Hörgeräten Lehrende anzeigen
    • Prof. Dr. Volker Hohmann, Dipl.-Phys.

    Donnerstag: 10:15 - 11:45, wöchentlich (ab 21.10.2021)

    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

  • Kein Zugang 5.04.4213 - Machine Learning I - Probabilistic Unsupervised Learning Lehrende anzeigen
    • Prof. Dr. Jörg Lücke

    Mittwoch: 10:15 - 11:45, wöchentlich (ab 20.10.2021), Ort: W32 0-005
    Termine am Dienstag, 08.03.2022 - Freitag, 11.03.2022 08:30 - 18:30, Montag, 14.03.2022 08:30 - 16:00, Dienstag, 15.03.2022 08:30 - 13:00, Montag, 30.05.2022 11:00 - 18:00, Dienstag, 31.05.2022 08:00 - 18:00, Ort: W01 0-012, W04 1-171

    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

  • Kein Zugang 5.04.4213 Ü1 - Machine Learning I - Probabilistic Unsupervised Learning Lehrende anzeigen
    • Prof. Dr. Jörg Lücke
    • Florian Hirschberger
    • Filippos Panagiotou

    Dienstag: 16:15 - 17:45, wöchentlich (ab 26.10.2021), Ort: W32 0-005, W32 1-112

  • Kein Zugang 5.04.4213 Ü2 - Machine Learning I - Probabilistic Unsupervised Learning Lehrende anzeigen
    • Prof. Dr. Jörg Lücke
    • Filippos Panagiotou
    • Florian Hirschberger

    Dienstag: 16:15 - 17:45, wöchentlich (ab 26.10.2021)

  • Kein Zugang 5.04.4213 Ü3 - Machine Learning I - Probabilistic Unsupervised Learning Lehrende anzeigen
    • Prof. Dr. Jörg Lücke
    • Dmytro Velychko

    Dienstag: 10:15 - 11:45, wöchentlich (ab 26.10.2021), Ort: W04 1-172, W01 0-012

  • Kein Zugang 5.04.4214 - Advanced Models and Algorithms in Machine Learning Lehrende anzeigen
    • Prof. Dr. Jörg Lücke

    Montag: 08:15 - 09:45, wöchentlich (ab 18.10.2021)

    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.

  • Kein Zugang 5.04.4586 - Advanced Topics Speech and Audio Processing Lehrende anzeigen
    • Prof. Dr. Simon Doclo

    Montag: 14:00 - 16:00, wöchentlich (ab 18.10.2021), online
    Donnerstag: 10:00 - 12:00, wöchentlich (ab 21.10.2021), online

    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).

  • Kein Zugang 5.04.663 - Akustische Messtechnik Lehrende anzeigen
    • Prof. Dr. Matthias Blau
    • Prof. Dr. Jörg Bitzer
    • Prof. Dr. Steven van de Par
    • Prof. Dr. Simon Doclo

    Montag: 10:15 - 11:45, wöchentlich (ab 18.10.2021)
    Donnerstag: 16:15 - 17:45, wöchentlich (ab 21.10.2021)

    The students acquire knowledge about advanced concepts in acoustics, electro-acoustics, room acoustics, acoustical measurement methods and virtual acoustics. The students acquire skills to critically and independently apply these concepts and methods to acoustical problems. Acoustical measurement methods (sound pressure, spectrum, transfer function, intensity); Non-linear measurement methods (Hammerstein model); Inverse problems in acoustics and regularization; High-resolution methods, acoustic camera; Binaural virtual acoustics; Spherical harmonics, virtual acoustics (Ambisonics, Wave Field Synthesis); Transaural systems; Room acoustics simulation.

  • Kein Zugang 5.04.813 - Cochlear Implants Lehrende anzeigen
    • Prof. Dr. Mathias Dietz

    Dienstag: 12:15 - 13:45, wöchentlich (ab 19.10.2021)

    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.

Übung
Hinweise zum Modul
Teilnahmevoraussetzungen
Bachelor in Hörtechnik und Audiologie oder entsprechend
Hinweise
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
Prüfungsleistung Modul
Klausur oder zwei Teilklausuren
oder mündliche Prüfung oder Präsentation (separate Teilprüfungen nur für Principles of Signal Processing in Hearing Devices bzw. Angewandte Psychophysik)
Kompetenzziele
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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