Lectures
Summer Term 2023
Lectures
Signals and Systems II
 Content: The lecture covers the following topics:
 Discrete signals
 Discretetime Fourier transform (DTFT)
 Discrete Fourier transform (DFT)
 ztransform
 Discrete LTI systems in the time domain
 Discrete LTI systems in the frequency domain
 Discrete LTI systems with special transfer functions
 Causal discrete LTI systems and Hilbert transform
 Stability of discrete LTI systems
 Description of stochastic signals
 Stochastic signals and LTI systems
 Lecturer: André Kaup
 Exercise: Christian Herglotz
 Tutorium: Hannah Och
 ECTS: 5.0
 SWS: 3.0
 Language: German
 Further information can be found on StudOn and Campo.
Image and Video Compression
 Content: The lecture gives an introduction to basic concepts and algorithms for coding and transmission of image and video signals. We start with sampling and digital representation of images and video signals and learn about fundamental features of human visual perception. Principles of data compression using redundancy and irrelevancy reduction are discussed in detail together with typical algorithms used for coding of image and video signals. Among others this covers the design of quantizers with the help of the LloydMax algorithm, entropy coding using Huffman and arithmetic coding, as well as run length coding. Moreover, fundamentals of vector quantization and predictive coding are addressed. Methods for frequency analysis will be discussed taking transform coding, subband decomposition and wavelet analysis as examples. The principle of motion compensation and hybrid video coding is introduced. Finally, current MPEG and ITU standards for coding of still images and video signals are explained.
 Lecturer: André Kaup
 Exercise: Fabian Brand
 ECTS: 5.0
 SWS: 3.0
 Language: English
 Further information can be found on StudOn and Campo.
Sprach und Audiosignalverarbeitung
 Content: The lecture concentrates on algorithms for speech and audio signal processing with applications in telecommunications and multimedia, especially

physiology and models for human speech production and hearing: sourcefilter model, filterbank model of the cochlea, masking effects,

representation of speech and audio signals: estimation and representation of shortterm and longterm statistics in the time and frequency domain as well as the cepstral domain; typical examples and visualizations

source coding for speech and audio signals: criteria, scalar and vector quantization, linear prediction, prediction of the pitch frequency; waveform coding, parametric coding, hybrid coding, codec standards (ITU, GSM, ISOMPEG)

basic concepts of automatic speech recognition (ASR): feature extraction, dynamic time warping, Hidden Markov Models (HMMs)

basic concepts of speech synthesis: texttospeech systems, modelbased and datadriven synthesis, PSOLA synthesis system

signal enhancement for acquisition and reproduction: noise reduction, acoustic echo cancellation, dereverberation using singlechannel and multichannel algorithms.

 Lecturer: Walter Kellermann
 Exercise: Heinrich Löllmann
 ECTS: 5.0
 SWS: 3.0
 Language: Englisch
 Further information can be found on StudOn and Campo.
Stochastische Prozesse
 Content:
 Calculus of probabilities and random variables
probability, random variables, uni and multivariate probability density functions and cumulative distribution functions; functions of random variables and their distributions and densities; expected values; special distributions (discrete and continuous); limit theorems  Stochastic processes
distributions, densities and expected values of onedimensional stochastic processes; stationarity, cyclostationarity, ergodicity; weakly stationary, continuoustime and discretetime processes in the time domain and in the frequency domain; linear timeinvariant (LTI) systems and weakly stationary processes 
Estimation theory
point estimation and interval estimation; estimation criteria; prediction; classical and Bayesian parameter estimation (incl. MMSE, Maximum Likelihood, Maximum A Posteriori); CramerRao bound; statistical hypothesis tests and decision processes (binary decisions, test statistics, ChiSquared test); binary decisions, NeymanPearson criterion 
Linear optimal filtering
principle of othogonality; continuoustime and discretetime Wiener filtering; adaptive filters (LMS, NLMS); continuoustime and discretetime matched filter
 Calculus of probabilities and random variables
 Lecturer: Walter Kellermann
 Exercise: Thomas Haubner
 ECTS: 5.0
 SWS: 3.0
 Language: German
 Further information can be found on StudOn and Campo.
Transformations in Signal Processing
 Content: The lecture “Transformations in Signal Processing” covers several different transforms which are used in the field of signal processing. For this, first the basic concepts of transforms are discussed and the advantages which are offered by the different transforms are presented. Subsequent to this, fundamental properties of integral transforms are considered and the Laplace and the FourierTransform are examined in detail. To be able to transform timevarying signals, the ShortTime FourierTransform and the GaborTransform are introduced, afterwards. Subsequent to this, the impact of sampling on transformed signals is analyzed before the zTransform as a transform for discrete signals is covered. Finally, further transforms for discrete signals like the Discrete FourierTransform or LinearBlock Transforms are discussed.
 Lecturer: Jürgen Seiler
 ECTS: 2.5
 SWS: 2.0
 Language: English
 Further information can be found on StudOn and Campo.
Introduction in Deep Learning
 Content: The students will learn the basics in deep learning, including classical neural network models and recent architectures. The students will acquire knowledge on processing different types of data with deep neural networks. In the exercises, the students will implement some of the standard models for classification or regression tasks and acquire knowledge on machine learning applications.
The lecture topics include:
 Learning from data, machine learning and deep learning
 Machine learning principles
 Artificial neural networks
 Convolutional neural networks
 Backpropagation
 Network optimization
 Initialisation, regularisation
 Deep network architectures
 Generative models
 Autoencoders
 Sequential models
 Deep learning applications
 Lecturer: Vasileios Belagiannis
 ECTS: 5
 SWS: 4
 Language: English
 More information can be found on StudOn and Campo.
Advanced Topics in Deep Learning
 Content: The students will learn advanced deep learning topics, including recent network architectures, generative models, selfsupervision, interpretability and explainability. In the exercises, the students will implement advanced models and techniques for classification or regression tasks.The lecture topics include:
 Geometric deep learning
 Attention and transformers
 Unsupervised and selfsupervised learning
 Generative models
 Interpretability
 Explainability
 Efficient Inference
 Uncertainty estimation
 Transfer learning and domain adaptation
 Fewshot learning
 Lecturer: Vasileios Belagiannis
 ECTS: 5
 SWS: 4
 Language: English
 More information can be found on StudOn and Campo.
Seminars
Selected Topics of Multimedia Communications and Signal Processing
 Content: The seminar deals with current research topics in the area of multimedia communications and signal processing. In an introductory meeting, the course of the seminar is outlined and each participant selects one of the offered topics. The participant should become familiar with the assigned research topic and present it by a report and a talk at the end of the seminar with the support of a supervisor.
 Organizer: Vasileios Belagiannis
 ECTS: 2.5
 SWS: 2.0
 Language: English
 Further information can be found on StudOn and Campo.
Lab courses
Image and Video Compression
 Content: The lab course focuses on video coding methods. During the lab course, each group will implement its own video codec and evaluate its properties. Knowledge in MATLAB is helpful but not required.
Structure
The lab course consists of four main parts:
Part 1: Introduction to MATLAB
Part 2: Processing blocks in video codecs (quantization, entropy coding, transformation, motion estimation)
Part 3: Integration of the blocks into a video codec pipeline and implementation of optional methods
Part 4: Subjective evaluation and analysisAdditional information
The content of this lab course is closely connected to the lecture Image and Video Compression (IVC).  Organizer: Christian Herglotz, Geetha Ramasubbu
 ECTS: 2.5
 SWS: 3.0
 Language: English
 Further information can be found on StudOn and Campo.
Machine Learning in Signal Processing
 Organizer: Vasileios Belagiannis, Amir ElGhoussani
 ECTS: 2.5
 SWS: 2.0
 Language: English
 More information can be found on StudOn and Campo.
Winter Term 2022/23
Lectures
Signals und Systems I
 Content: Die Lehrveranstaltung führt in die Beschreibung von kontinuierlichen Signalen und kontinuierlichen zeitinvarianten linearen Systemen ein. Zunächst werden elementare kontinuierliche Signale, der DeltaImpuls, das Faltungsintegral und die Korrelation von Signalen erläutert. Anschließend wird die Frequenzbereichsdarstellung von Signalen mit Hilfe der Fourier und die LaplaceTransformation eingeführt einschließlich der Theoreme und Korrespondenzen dieser Transformationen. Es folgt die Beschreibung von kontinuierlichen linearen zeitinvarianten Systemen im Zeitbereich durch Impulsantwort und Faltung, Differentialgleichungen und die Zustandsraumdarstellung. Die Systembeschreibung im Frequenzbereich durch Eigenfunktionen, Übertragungs und Systemfunktion und Zustandsraumdarstellung wird erläutert, ebenso wie die Betrachtung von kontinuierlichen linearen zeitinvarianten Systemen mit Anfangsbedingungen. Nach der Vorstellung von linearphasigen, minimalphasigen, idealisierten Systemen und Allpässen werden Kausalität und HilbertTransformation, Stabilität und rückgekoppelte Systeme diskutiert. Die Vorlesung schließt mit der Betrachtung von Abtastsystemen und dem Abtasttheorem für Tiefpass und Bandpasssignale.
 Lecturer: André Kaup
 Exercise: Frank Sippel
 Tutorium: Maximiliane Gruber
 ECTS: 5.0
 SWS: 3.0
 Language: German
 More information can be found on StudOn and Campo.
Image, Video, and Multidimensional Signal Processing
 Content: The lecture gives an introduction to the basics of image and video signal processing. First, point operations, morphological filters, and color spaces including trichromaticity are explained. Subsequently, the theory of multidimensional signals and systems is introduced and Wiener filtering for image signals is derived. Based on this, interpolation methods for images such as bicubic and spline interpolation are explained. This is followed by methods for feature detection in images using Hough transforms and edge detection, and the principle of scaleinvariant features is explained. For video signals, motion estimation methods such as optical flow and image matching algorithms using SIFT and SURF are explained. Finally, the theory of image and Video segmentation using statistical methods is introduced, and transformbased methods for image processing are presented.
 Lecturer: André Kaup
 Exercise: Andy Regensky
 ECTS: 5.0
 SWS: 3.0
 Language: German
 More information can be found on StudOn and Campo.
Kommunikationsnetze
 Content: The lecture gives an introduction to fundamental concepts and mechanisms of digital communication networks. After explanation of some basic terms we first introduce hierarchical structuring of networks leading to the OSI reference model. Subsequent to discussion of data transmission from point to point, data link protocols for errorfree transmission are discussed, especially forward error control and ARQ protocols. Protocols for multiple access control follow, among them ALOHA protocols, strategies to detect and resolve collisions, carrier sensing, as well as token passing principles. Subsequently, fundamental routing algorithms are explained. After an introduction into queuing theory, the lecture gives an overview of the internet protocol suite TCP/IP as important system example and closes with an analysis of multimedia networks.
 Lecturer: André Kaup
 Exercise: Matthias Kränzler
 ECTS: 5.0
 SWS: 3.0
 Language: German
 More information can be found on StudOn and Campo.
Digital Signal Processing
 Content: The course assumes familiarity with basic theory of discretetime deterministic signals and linear systems and extends this by a discussion of the properties of idealized and causal, realizable systems (e.g., lowpass, Hilbert transformer) and corresponding representations in the time domain, frequency domain, and zdomain. Thereupon, design methods for recursive and nonrecursive digital filters are discussed. Recursive systems with prescribed frequencydomain properties are obtained by using design methods for Butterworth filters, Chebyshev filters, and elliptic filters borrowed from analog filter design. Impulseinvariant transform and the Pronymethod are representatives of the considered designs with prescribed timedomain behaviour.For nonrecursive systems, we consider the Fourier approximation in its original and its modified form introducing a broad selection of windowing functions. Moreover, the equiripple approximation is introduced based on the Remezexchange algorithm. Another section is dedicated to the Discrete Fourier Transform (DFT) and the algorithms for its fast realizations (‘Fast Fourier Transform’). As related transforms we introduce cosine and sine transforms. This is followed by a section on nonparametric spectrum estimation. Multirate systems and their efficient realization as polyphase structures form the basis for describing analysis/synthesis filter banks and discussing their applications. The last section is dedicated to investigating effects of finite wordlength as they are unavoidable in any realization of digital signal processing systems. A corresponding lab course on DSP will be offered in the winter term.
 Lecturer: Walter Kellermann
 Exercise: Heinrich Löllmann
 ECTS: 5.0
 SWS: 3.0
 Language: English
 More information can be found on StudOn and Campo.
Statistical Signal Processing
 Content:
 DiscreteTime random processes in time domain and frequency domain
 Random variables (RVs): probability distributions and densities, expectations; transformation of RVs; vectors of normally distributed RVs; discretetime random processes (RPs): probability distributions and densities, expectations; stationarity, cyclostationarity, ergodicity, correlation functions and matrices, frequencydomain representations; principal component analysis, KarhunenLoeve transform;
 Estimation theory
 Estimation criteria; prediction; classical and Bayes estimation (incl. MMSE, Maximum Likelihood, Maximum A Posteriori); CramerRao bound
 Linear signal models
 Nonparametric models (cepstral decomposition, PaleyWiener theorem, spectral flatness); Parametric models: allpole /allzero/polezero(AR/MA/ARMA) models; lattice structures, YuleWalker equations, PARCOR coefficients, cepstral representation;
 Signal estimation
 Supervised signal estimation, problem classfication; orthogonality principle, MMSE estimation, linear MMSE estimation for normal processes; optimum FIR filters; optimum lineare filters for stationary RPs; prediction, filtering, smoothing; Kalman filter; optimum multichannel filters (Wiener filter, LCMV, MVDR, GSC);
 Adaptive Filtering
 Gradient descent; LMS, NLMS , APA and RLS algorithm and its convergence
 Lecturer: Walter Kellermann
 Exercise: Thomas Haubner
 ECTS: 5.0
 SWS: 3.0
 Language: English
 More information can be found on StudOn and Campo.
Machine Learning in Signal Processing
 Content: The students will learn the basics of machine learning, including theory and applications. The students will acquire knowledge on processing different types of data and signals with machine learning algorithms. In the exercises, the students will work on regression and classification assignments as well as different machine learning approaches.
The lecture topics include:
 Machine learning basics
 Linear regression
 Linear classification
 Performance metrics
 Decision trees
 Random forests
 Support vector machines
 Neural networks
 Clustering
 Lecturer: Vasileios Belagiannis
 Exercise: Kamal Nambiar
 ECTS: 5.0
 SWS: 3.0
 Language: English
 More information can be found on StudOn and Campo.
Introduction in Deep Learning
 Content: The students will learn the basics in deep learning, including classical neural network models and recent architectures. The students will acquire knowledge on processing different types of data with deep neural networks. In the exercises, the students will implement some of the standard models for classification or regression tasks and acquire knowledge on machine learning applications.
The lecture topics include:
 Learning from data, machine learning and deep learning
 Machine learning principles
 Artificial neural networks
 Convolutional neural networks
 Backpropagation
 Network optimization
 Initialisation, regularisation
 Deep network architectures
 Generative models
 Autoencoders
 Sequential models
 Deep learning applications
 Lecturer: Vasileios Belagiannis
 ECTS: 5
 SWS: 4
 Language: English
 More information can be found on StudOn and Campo.
Music Processing  Synthesis
 Content: The lecture covers digital processing of audio signals with parametric filters and effects as well as sound synthesis for musical applications. Various sound examples demonstrate the contents of the lecture. *Filters and Effects* – parametric filters: structure and design – digital sound effects *Digital Sound Synthesis* – a short history of computer music – wavetable synthesis – spectral synthesis – physical modelling *Systems for Sound Production and Reproduction* – sound effects – synthesizer and vocoder – artificial reverberation
 Lecturer: Maximilian Schäfer
 ECTS: 2.5
 SWS: 2.0
 Language: English
 More information can be found on StudOn and Campo.
Signal Analysis
 Content: In this course, different approaches for the analysis of digital signals and their applications are treated, which comprises the following topics:

Fourier analysis of signals

Signal analysis by means of timefrequency transformations

Parametric and nonparametric signal analysis

Frequency estimation

Spatial signal analysis

Filterbanks and wavelets.

 Lecturer: Heinrich Löllmann
 ECTS: 5.0
 SWS: 2.0
 Language: English
 More information can be found on StudOn and Campo.
Transformations in Signal Processing
 Content: The lecture covers several different transforms which are used in the field of signal processing. For this, first the basic concepts of transforms are discussed and the advantages which are offered by the different transforms are presented. Subsequent to this, fundamental properties of integral transforms are considered and the Laplace and the FourierTransform are examined in detail. To be able to transform timevarying signals, the ShortTime FourierTransform and the GaborTransform are introduced, afterwards. Subsequent to this, the impact of sampling on transformed signals is analyzed before the zTransform as a transform for discrete signals is covered. Finally, further transforms for discrete signals like the Discrete FourierTransform or LinearBlock Transforms are discussed.
 Lecturer: Jürgen Seiler
 ECTS: 2.5
 SWS: 2.0
 Language: English
 More information can be found on StudOn and Campo.
Virtual Vision
 Content: The lecture „Virtual Vision“ discusses basic knowledge and the state of the art in immersive visual technologies. To this end, the light field function is taken as a basis, which describes light in a mathematical sense. It covers different physical aspects of light such as brightness, color, incident angle, timing, and position of the viewer. These aspects are all discussed in detail with a special focus on the human visual system, capturing of light, processing and compression of light signals, and finally the display of visual data. In addition, we discuss the power and energy consumption of modern visual systems and how they can be reduced. At the end of the lecture, students will have a broad knowledge on important properties of light and be able to understand visual systems enabling virtual reality experiences.
 Lecturer: Christian Herglotz
 ECTS: 2.5
 SWS: 2.0
 Language: English
 More information can be found on StudOn and Campo.
Seminars
Selected Topics in Multimedia Communications and Signal Processing
 Content: This seminar is designed for Bachelor and Master programs in Electrical Engineering, Electronics and Information Technology (EEI), Information and Communication Technology (IuK), Communications and Multimedia Engineering (CME), Advanced Signal Processing and Communications Engineering (ASC) as well as related study programs.
The students will study, understand, and present scientific publications from the recent literature on applied artificial intelligence, machine learning and deep learning. After finishing the seminar, the student will be able to summarise and present a publication.The topic of the seminar in the winter term 2022/23 is Trends in Applied Artificial Intelligence, Machine Learning and Deep Learning and it addresses a wide range of research topics in artificial intelligence, machine learning and deep learning, with applications in topics such as robotics, medical imaging, and computer vision.
 Organizer: Vasileios Belagiannis, Kamal Nambiar
 ECTS: 2.5
 SWS: 2.0
 Language: English
 More information can be found on StudOn and Campo.
Lab courses
Image and Video Signal Processing on Embedded Systems (BiViP)
 Content: Today, many image and video signal processing applications are running on embedded systems. However, the computational power and the energy storage is a limiting demand for embedded systems. Nevertheless, daily mobile devices like smartphone and tablet are able to perform signal processing tasks for image and video signals, for example coding of images and videos, the creation of a panorama or the calculation of images with high dynamic range. The image and video signal processing on embedded systems lab course should show the challenges that occur while handling with such mobile devices and the implementation of such algorithm on an embedded system. Therefore, Raspberry Pis as embedded systems and Python as coding language is used in the laboratory. The experiments include the setup of the Raspberry Pi, an introduction to Python and an introduction to image and video signal processing. In addition, a camera will be connected, signal processing will be done with the camera and digital filters are implemented. Moreover, the laboratory includes different computer vision applications like the creation of a panorama.
 Organizer: Jürgen Seiler, Viktoria Heimann
 ECTS: 2.5
 SWS: 2.0
 Language: English
 More information can be found on StudOn and Campo.
Digital Signal Processing Laboratory
 Content: In this laboratory course the theory from the lecture Digital Signal Processing is applied in practice, using the programming environment MATLAB. The topics include quantization, spectral analysis, FIR and IIR filter design, filter banks and adaptive filters. The course consists of 5 guided experiments in which students work on programming problems in groups of two, and a group project in which each group works on an individual project from the field of digital signal processing. The preparation, as well as the results of the past experiment will be examined by a short test at the beginning of each experiment. For passing the lab course, a minimum number of points from the tests and the project is required. The course requires previous experience in MATLAB programming. It is possible take the course in parallel to the DSP lecture, however, revision of the relevant lecture contents before each lab lesson, and participation in the DSP exercises and tutorials is required.
 Organizer: Matthias Kreuzer, Heinrich Löllmann
 ECTS: 2.5
 SWS: 2.0
 Language: English
 More information can be found on StudOn and Campo.
Statistical Signal Processing
 Content: After an introduction to scientific programming with Python, experiments and exercises related to the following topics are carried out during the laboratory course:
 Fundamental properties of random variables and stochastic processes
 Properties of correlations matrices, Principal Component Analysis, KLT
 Parametric and nonparametric linear signal models
 MMSE signal estimation
 Kalman filtering with applications to source tracking
 Optimum multichannel filtering
 Introduction to adaptive filtering.
In the second part of the lab course, the students will work independently on a research question and develop, implement and evaluate possible solutions in small groups (max. 3 students).
 Organizer: Annika Briegleb
 ECTS: 2.5
 SWS: 2.0
 Language: English
 More information can be found on StudOn and Campo.
SimulationsTools
Das Praktikum wird im Rahmen der „Arbeitstechnik“ angeboten.
 Content: Pro SimToolsTermin (V1, V2,…) gibt es zwei Gruppen (A, B)*. Die Gruppenaufteilung für die Workshops und das Praktikum SimTools erfolgt im Rahmen der Anmeldung in MeinCampus. Falls es für eine Gruppe mehr Anmeldungen als Plätze gibt, so wird den Teilnehmern auf der Warteliste ein Platz in einer anderen Gruppe angeboten.
 Termine
 09.12.2022: Gruppe A, 812 Uhr: V1 Einführung in MATLAB Gruppe B, 1216 Uhr: V1 Einführung in MATLAB
 16.12.2022: Gruppe A, 812 Uhr: V2 Grafische Ausgabe Gruppe B, 1216 Uhr: V2 Graphische Ausgabe
 13.01.2023: Gruppe A, 812 Uhr: V3 Matrizen & lineare Gleichungssysteme Gruppe B, 1216 Uhr: V3 Matrizen & lineare Gleichungssysteme
 20.01.2023: Gruppe A, 812 Uhr: V4 Komplexe Zahlen Gruppe B, 1216 Uhr: V4 Komplexe Zahlen
 27.01.2023, SimTools Nachholtermin für alle Gruppen von 8 – 12 Uhr
 Termine
 Organizer: Heinrich Löllmann
 SWS: 2.0
 Language: German
 More information can be found on StudOn and Campo.