Module CS5260-KP04, CS5260SJ14

Duration

1 Semester

Turnus of offer

every second semester

Credit points

4

Course of studies, specific fields and terms:

• Master CLS 2023, optional subject, Elective
• Master Robotics and Autonomous Systems 2019, optional subject, Additionally recognized elective module
• Master MES 2020, optional subject, medical engineering science
• Master Media Informatics 2020, optional subject, computer science
• Master Medical Informatics 2019, optional subject, Medical Data Science / Artificial Intelligence
• Master MES 2014, optional subject, medical engineering science
• Master CLS 2010, optional suject, computer science
• Master Medical Informatics 2014, optional subject, computer science
• Master Media Informatics 2014, optional subject, computer science

Classes and lectures:

• Speech and Audio Signal Processing (exercise, 1 SWS)
• Speech and Audio Signal Processing (lecture, 2 SWS)

Workload:

• 55 hours private studies
• 45 hours in-classroom work
• 20 hours exam preparation

Contents of teaching:

• Speech production and human hearing
• Physical models of the auditory System
• Dynamic compression
• Spectral analysis: Spectrum and cepstrum
• Spectral perception and masking
• Vocal tract models
• Linear prediction
• Coding in time and frequency domains
• Speech synthesis
• Noise reduction and echo compensation
• Source localization and spatial reproduction
• Basics of automatic speech recognition

Qualification-goals/Competencies:

• Students are able to describe the basics of human speech production and the corresponding mathematical models.
• They are able to describe the process of human auditory perception and the corresponding signal processing tools for mimicing auditory perception.
• They are able to present basic knowledge of statistical speech modeling and automatic speech recognition.
• They can describe and use signal processing methods for source separation and room-acoustic measurements.

Grading through:

• Written or oral exam as announced by the examiner

Responsible for this module:

• Prof. Dr.-Ing. Markus Kallinger

Teacher:

• Institute for Signal Processing
• Prof. Dr.-Ing. Markus Kallinger

Literature:

• L. Rabiner, B.-H. Juang : Fundamentals of Speech Recognition Upper Saddle River: Prentice Hall 1993
• J. O. Heller, J. L. Hansen, J. G. Proakis : Discrete-Time Processing of Speech Signals IEEE Press

Language:

• offered only in German

Notes:

Last Updated:

26.02.2026