Stud.IP Uni Oldenburg
University of Oldenburg
28.06.2022 11:30:48
phy830 - Acoustics and Signal Processing Part I
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Module label Acoustics and Signal Processing Part I
Module code phy830
Credit points 6.0 KP
Workload 180 h
Präsenzzeit: 56 Stunden Selbststudium: 124 Stunden
Institute directory Institute of Physics
Applicability of the module
  • Master's Programme Hearing Technology and Audiology (Master) > Mastermodule
Responsible persons
Hohmann, Volker (Module counselling)
Anemüller, Jörn (Module counselling)
Jürgens, Tim (Module counselling)
Lücke, Jörg (Module counselling)
Blau, Matthias (Authorized examiners)
Brand, Thomas (Authorized examiners)
Dietz, Mathias (Authorized examiners)
Doclo, Simon (Authorized examiners)
Ewert, Stephan (Authorized examiners)
Kollmeier, Birger (Authorized examiners)
Meyer, Bernd (Authorized examiners)
Schädler, Marc René (Authorized examiners)
Uppenkamp, Stefan (Authorized examiners)
van de Par, Steven (Authorized examiners)
Bachelor in Hörtechnik und Audiologie oder entsprechend
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.
Module contents
Advanced Topics Speech and Audio Processing: After reviewing basic principles of speech processing and statistical signal processing (adaptive filtering), this course covers techniques and underlying algorithms that are essential in many modern-day speech communication and audio processing systems: acoustic echo and feedback cancellation, noise reduction, dereverberation, microphone and loudspeaker array processing, active noise control, time-stretching and pitch-shifting, audio restoration. Angewandte Psychophysik: Subjective listening experiment design and models of human auditory perception will be treated with a focus on application in sound quality measurement (e.g. for vehicle noise and sound reproduction) and in digital signal processing algorithm development (e.g. for low bit-rate audio coding and headphone virtualizers). Machine Learning I: 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. Principles of Signal Processing in Hearing Devices: - 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 Cochlear Implats: Funktionsweise und Signalverarbeitung von CIs, Elektrisch evozierte Neuronenaktivierung Perzeption mit CI Anpassung eines CI Sonderfälle: bilaterales CI, bimodal, elektroakustisch, Hirnstamm Implantate Oberseminar Akustik Aktuelle Forschungsarbeiten der Akustik
Reader's advisory
- H. Dillon, Hearing-Aids, Thieme Verlag - Brandstein, Ward (Eds.): Microphone Arrays, Springer Verlag, 2001. - M. R. Schroeder: Computer Speech, Springer, Berlin, 1999. - J. R. Deller, J. H. L. Hansen, J. G. Proakis: Discrete-Time Processing of Speech Signals, Wiley-IEEE Press, 1999. - J. Benesty, M. M. Sondhi, Y. Huang (Eds.): Handbook of Speech Processing, Springer, 2008. - P. Loizou: Speech Enhancement: Theory and Practice, CRC Press, 2007. - Gold, Morgan: Speech and Audio Signal Processing, 2000. - U. Zölzer (editor): DAFx Digital Audio Effects, Wiley, 2002. - S. Haykin: Adaptive Filter Theory, Prentice Hall, 2013. - C. M. Bishop, Pattern Recognition and Machine Learning, Springer 2006. (best suited for lecture). - D. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003. (free online) - Schaub (2008) Digital Hearing Aids, Thieme Publishers - V. Hamacher et al. (2005) Signal processing in high-end hearing aids: state of the art, challenges, and future trends. EURASIP Journal on Applied Signal Processing - K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012. - K. Petersen, M. Pederson, The Matrix Cookbook, (free online)
Languages of instruction German, English
Duration (semesters) 1 Semester
Module frequency Wintersemester
Module capacity unlimited
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
Modullevel / module level MM (Mastermodul / Master module)
Modulart / typ of module je nach Studiengang Pflicht oder Wahlpflicht
Lehr-/Lernform / Teaching/Learning method
Vorkenntnisse / Previous knowledge
Examination Time of examination Type of examination
Final exam of module
Course type Seminar
Frequency --
Workload attendance 0 h