• Content: The lecture covers the following topics:
  • Discrete signals
  • Discrete-time Fourier transform (DTFT)
  • Discrete Fourier transform (DFT)
  • z-transform
  • 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.

• 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 Lloyd-Max 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.

• 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: source-filter model, filterbank model of the cochlea, masking effects,

  • representation of speech and audio signals: estimation and representation of short-term and long-term 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, ISO-MPEG)

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

  • basic concepts of speech synthesis: text-to-speech systems, model-based and data-driven synthesis, PSOLA synthesis system

  • signal enhancement for acquisition and reproduction: noise reduction, acoustic echo cancellation, dereverberation using single-channel 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.

• 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 one-dimensional stochastic processes; stationarity, cyclostationarity, ergodicity; weakly stationary, continuous-time and discrete-time processes in the time domain and in the frequency domain; linear time-invariant (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); Cramer-Rao bound; statistical hypothesis tests and decision processes (binary decisions, test statistics, Chi-Squared test); binary decisions, Neyman-Pearson criterion

  • Linear optimal filtering
    principle of othogonality; continuous-time and discrete-time Wiener filtering; adaptive filters (LMS, NLMS); continuous-time and discrete-time matched filter

• Lecturer: Walter Kellermann
• Exercise: Thomas Haubner
• ECTS: 5.0
• SWS: 3.0
• Language: German
• Further information can be found on StudOn and Campo.

• 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 Fourier-Transform are examined in detail. To be able to transform time-varying signals, the Short-Time Fourier-Transform and the Gabor-Transform are introduced, afterwards. Subsequent to this, the impact of sampling on transformed signals is analyzed before the z-Transform as a transform for discrete signals is covered. Finally, further transforms for discrete signals like the Discrete Fourier-Transform or Linear-Block Transforms are discussed.
• Lecturer: Jürgen Seiler
• ECTS: 2.5
• SWS: 2.0
• Language: English
• Further information can be found on StudOn and Campo.

• 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
  • Back-propagation
  • Network optimization
  • Initialisation, regularisation
  • Deep network architectures
  • Generative models
  • Auto-encoders
  • Sequential models
  • Deep learning applications
• Lecturer: Vasileios Belagiannis
• ECTS: 5
• SWS: 4
• Language: English
• More information can be found on StudOn and Campo.

• Content: The students will learn advanced deep learning topics, including recent network architectures, generative models, self-supervision, 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 self-supervised learning
  • Generative models
  • Interpretability
  • Explainability
  • Efficient Inference
  • Uncertainty estimation
  • Transfer learning and domain adaptation
  • Few-shot learning
• Lecturer: Vasileios Belagiannis
• ECTS: 5
• SWS: 4
• Language: English
• More information can be found on StudOn and Campo.

• 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.

• 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 analysis

  Additional 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.

• Organizer: Vasileios Belagiannis, Amir El-Ghoussani
• ECTS: 2.5
• SWS: 2.0
• Language: English
• More information can be found on StudOn and Campo.

• Content: Die Lehrveranstaltung führt in die Beschreibung von kontinuierlichen Signalen und kontinuierlichen zeitinvarianten linearen Systemen ein. Zunächst werden elementare kontinuierliche Signale, der Delta-Impuls, das Faltungsintegral und die Korrelation von Signalen erläutert. Anschließend wird die Frequenzbereichsdarstellung von Signalen mit Hilfe der Fourier- und die Laplace-Transformation 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 Hilbert-Transformation, 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.

• Content: The lecture gives an introduction to the basics of image and video signal processing. First, point operations, morphological filters, and color spaces including tri-chromaticity 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 scale-invariant 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 transform-based 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.

• 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.

• Content: The course assumes familiarity with basic theory of discrete-time 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 z-domain. Thereupon, design methods for recursive and nonrecursive digital filters are discussed. Recursive systems with prescribed frequency-domain properties are obtained by using design methods for Butterworth filters, Chebyshev filters, and elliptic filters borrowed from analog filter design. Impulse-invariant transform and the Prony-method are representatives of the considered designs with prescribed time-domain 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 Remez-exchange 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.

• Content: 
  • Discrete-Time random processes in time domain and frequency domain
  • Random variables (RVs): probability distributions and densities, expectations; transformation of RVs; vectors of normally distributed RVs; discrete-time random processes (RPs): probability distributions and densities, expectations; stationarity, cyclostationarity, ergodicity, correlation functions and matrices, frequency-domain representations; principal component analysis, Karhunen-Loeve transform;
  • Estimation theory
  • Estimation criteria; prediction; classical and Bayes estimation (incl. MMSE, Maximum Likelihood, Maximum A Posteriori); Cramer-Rao bound
  • Linear signal models
  • Nonparametric models (cepstral decomposition, Paley-Wiener theorem, spectral flatness); Parametric models: allpole /allzero/pole-zero-(AR/MA/ARMA) models; lattice structures, Yule-Walker 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.

• 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.

• 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
  • Back-propagation
  • Network optimization
  • Initialisation, regularisation
  • Deep network architectures
  • Generative models
  • Auto-encoders
  • Sequential models
  • Deep learning applications
• Lecturer: Vasileios Belagiannis
• ECTS: 5
• SWS: 4
• Language: English
• More information can be found on StudOn and Campo.

• 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.

• 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 time-frequency transformations

  • Parametric and non-parametric signal analysis

  • Frequency estimation

  • Spatial signal analysis

  • Filter-banks and wavelets.

• Lecturer: Heinrich Löllmann
• ECTS: 5.0
• SWS: 2.0
• Language: English
• More information can be found on StudOn and Campo.

• 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 Fourier-Transform are examined in detail. To be able to transform time-varying signals, the Short-Time Fourier-Transform and the Gabor-Transform are introduced, afterwards. Subsequent to this, the impact of sampling on transformed signals is analyzed before the z-Transform as a transform for discrete signals is covered. Finally, further transforms for discrete signals like the Discrete Fourier-Transform or Linear-Block Transforms are discussed.
• Lecturer: Jürgen Seiler
• ECTS: 2.5
• SWS: 2.0
• Language: English
• More information can be found on StudOn and Campo.

• 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.

• 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.

• 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.

• 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.

• 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 non-parametric 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.

Das Praktikum wird im Rahmen der „Arbeitstechnik“ angeboten.

• Content: Pro SimTools-Termin (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, 8-12 Uhr: V1 Einführung in MATLAB Gruppe B, 12-16 Uhr: V1 Einführung in MATLAB
    • 16.12.2022: Gruppe A, 8-12 Uhr: V2 Grafische Ausgabe Gruppe B, 12-16 Uhr: V2 Graphische Ausgabe
    • 13.01.2023: Gruppe A, 8-12 Uhr: V3 Matrizen & lineare Gleichungssysteme Gruppe B, 12-16 Uhr: V3 Matrizen & lineare Gleichungssysteme
    • 20.01.2023: Gruppe A, 8-12 Uhr: V4 Komplexe Zahlen Gruppe B, 12-16 Uhr: V4 Komplexe Zahlen
    • 27.01.2023, SimTools Nachholtermin für alle Gruppen von 8 – 12 Uhr
• Organizer: Heinrich Löllmann
• SWS: 2.0
• Language: German
•  More information can be found on StudOn and Campo.