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 2021/2022 Examination
  • No access 5.04.4203 - Angewandte Psychophysik: Anwendungen bei Audioqualitätsbewertungen / Applied Psychophysics: Applications in audio quality Show lecturers
    • Prof. Dr. Steven van de Par
    • Stephan Töpken

    Wednesday: 16:15 - 17:45, weekly (from 20/10/21)

    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).

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

    Thursday: 10:15 - 11:45, weekly (from 21/10/21)

    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: 10:15 - 11:45, weekly (from 20/10/21), Location: W32 0-005
    Dates on Tuesday, 08.03.2022 - Friday, 11.03.2022 08:30 - 18:30, Monday, 14.03.2022 08:30 - 16:00, Tuesday, 15.03.2022 08:30 - 13:00, Monday, 30.05.2022 11:00 - 18:00, Tuesday, 31.05.2022 08:00 - 18:00, Location: 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:

  • No access 5.04.4213 Ü1 - Machine Learning I - Probabilistic Unsupervised Learning Show lecturers
    • Prof. Dr. Jörg Lücke
    • Florian Hirschberger
    • Filippos Panagiotou

    Tuesday: 16:15 - 17:45, weekly (from 26/10/21), Location: W32 0-005, W32 1-112

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

    Tuesday: 16:15 - 17:45, weekly (from 26/10/21)

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

    Tuesday: 10:15 - 11:45, weekly (from 26/10/21), Location: W04 1-172, W01 0-012

  • 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 18/10/21)

    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 5.04.4586 - Advanced Topics Speech and Audio Processing Show lecturers
    • Prof. Dr. Simon Doclo

    Monday: 14:00 - 16:00, weekly (from 18/10/21), online
    Thursday: 10:00 - 12:00, weekly (from 21/10/21), 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).

  • No access 5.04.663 - Akustische Messtechnik Show lecturers
    • Prof. Dr. Matthias Blau
    • Prof. Dr. Jörg Bitzer
    • Prof. Dr. Steven van de Par
    • Prof. Dr. Simon Doclo

    Monday: 10:15 - 11:45, weekly (from 18/10/21)
    Thursday: 16:15 - 17:45, weekly (from 21/10/21)

    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.

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

    Tuesday: 12:15 - 13:45, weekly (from 19/10/21)

    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.

Hinweise zum Modul
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)

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
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.