Lecture "Pattern Recognition and Machine Learning"


Basic Information
Lecturers: Gerhard Schmidt (lecture) and Erik Engelhardt (exercise)
Room: KS2/Geb.F - SR-III
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Language: English
Target group: Students in electrical engineering and computer engineering
Prerequisites: Basics in system theory

In this lecture the basics of speech, audio, and music signal processing are treated. Often schemes that are based on statistical optimization are utilized for these applications. The involved cost function are matched to the human audio perception.

Topic overview:

  • Preprocessing to reduce signal distortions
    • Noise reduction
    • Beamforming
  • Feature extraction and data compression
  • Pattern recognition and data regression
    • Learning types
      • Unsupervised learning
      • Supervised learning
      • Reinformcement learning
    • Codebooks
    • Gaussian mixture models (GMMs)
    • Artificial Neural Networks (ANNs)
      • Multilayer perceptron
      • Convolutional neuronal networks (CNNs)
      • Recurrent neural networks (RNNs)
      • Autoencoder networks
      • Generative adversarial networks (GANs)
    • Hidden Markov models (HMMs)
    • Explainable artificial intelligence (EAI)



A preliminary lecture and exercise schedule for WS 22/23 is now available.

The lecture will be given in seminar room KS2/Geb.F - SR-III and can be attended according to the university's current rules.

There is no exercise after the first lecture. Instead, the time allocated for the exercise will be used for the continuation of the lecture.

Remember to register for the exam in the QiS system. Without such a registration we will have to cancel any exam slot you booked with us. Booking of exam slots is possible here.



The following schedule regarding lectures and excercises is preliminary and may be adapted during the semester. The lecture will usually take place from 8:15 h - 10:45 h.

Date Lecture Exercise
26.10.2022 Introduction -
02.11.2022 Noise Suppression + Beamforming Noise Suppression (video)
09.11.2022 Beamforming + Feature Extraction Beamforming (video)
16.11.2022 Feature Extraction + Codebook Training Feature Extraction (video)
23.11.2022 Codebook Training + Bandwidth Extension Codebook Training (video)
30.11.2022 Bandwidth Extension Bandwidth Extension (video) + Question time with content per request (start time will be approx. 10:45 h)
07.12.2022 Gaussian Mixture Models Gaussian Mixture Models (video)
14.12.2022 Student Talks Student talks
11.01.2023 Neural Networks Neural Networks (video)
18.01.2023 Neural Networks Question time with content per request (start time will be approx. 10:45 h)
25.01.2023 Hidden Markov Models Hidden Markov Models (video)
01.02.2023 Explainable artificial intelligence Speaker and Speech Recognition (video) + Question time with content per request (start time will be approx. 10:45 h)


Lecture Slides

Link Content
Slides of the lecture "Introduction"
(Introduction, boundary conditions of the lecture, applications)
Slides of the lecture "Noise Suppression"
(Noise suppression, dereverberation, speech reconstruction)
Slides of the lecture "Beamforming"
(Fixed and adaptive beamforming, postfiltering)
Slides of the lecture "Feature Extraction"
(Linear prediction, cepstrum, mel-filtered cepstral coefficients)
Slides of the lecture "Codebook Training"
(K-means algorithm, LBG algorithms)
Slides of the lecture "Bandwidth Extension"
(Model-bases approaches, evaluation)
Slides of the lecture "Gaussian Mixture Models (GMMs)"
(Training with the EM algorithm, applications)
Slides of the lecture "Neural networks"
(Network types, training procecures)
Slides of the lecture "Hidden Markov Models (HMMs)"
(Efficient probability calculation, training of HMMs)
Slides of the lecture "Explainable artificial intelligence"
(Understanding what had contributed mainly to the decision of neural networks, etc.)


Matlab Demos

Link Content
Matlab demo (GUI based) for adaptive noise suppression
Matlab demo (GUI based) for linear prediction



For each lecture topic on-demand video will be provided. On 30.11.2021 and on 01.02.2022 there will be an in-presence exercise to discuss your questions and requested topics. You may send in questions (if you want the answer to be supported by slides) or exercise topic suggestions to This email address is being protected from spambots. You need JavaScript enabled to view it. at any time.

Video Content Material

Noise suppression:

  • Wiener Filter summary
  • comprehension questions
  • python demo


  • beamforming summary
  • comprehension questions
  • python demo

Feature extraction:

  • linear prediction
  • LPC, LPCC, MFCC features
  • comprehension questions
  • python demo

Codebook training:

  • codebooks
  • codebook training
  • comprehension questions
  • python demo

Bandwidth extension:

  • bandwidth extension summary
  • comprehension questions
  • python demo

Gaussian Mixture Models:

  • Gaussian Mixture Models
  • EM algorithm
  • comprehension questions
  • python demo

Neural Networks:

  • neural networks summary
  • comprehension questions
  • python demo

Hidden Markov Models:

  • hidden markov models summary
  • comprehension questions
  • python demo

Speaker and Speech Recognition:

  • speaker and speech recognition summary
  • comprehension questions
  • python demo



Each student will give a talk about a certain topic. The aim is both to give you the chance to work on a pattern recognition-related topic that interests you, and to improve your presentational skills. The talk is also a prerequisite for your admission to the exam. The talks should be held in English and should take ten minutes, plus 2.5 minutes of discussion and 2.5 minutes of feedback. Please write an email to This email address is being protected from spambots. You need JavaScript enabled to view it. to reserve your topic. The talk registration deadline is 7.12.2022.

Below you can find the preliminary schedule of the talks.

Date Room Time Topic Presenter(s)
14.12.2022 KS2/Geb.F - SR-III 08:20 h A Neural Network Based Approach for Diagnosis of Breast Cancer Usama Adeel
14.12.2022 KS2/Geb.F - SR-III 08:35 h Gato - A Generalist Agent Arber Ramadani
14.12.2022 KS2/Geb.F - SR-III 08:50 h Using Supervised and Unsupervised Learning for Improving Network Security in IoT Aida Habibipoor
14.12.2022 KS2/Geb.F - SR-III 09:05 h Support Vector Machines: Theory and Application Marten Finch and Luca Lohmann
14.12.2022 KS2/Geb.F - SR-III 09:35 h Break  
14.12.2022 KS2/Geb.F - SR-III 09:45 h Viola-Jones Face Detection Algorithm Torben Kannengießer and Tom Jürgensen
14.12.2022 KS2/Geb.F - SR-III 10:15 h Recognition of Deseases Based on VOC Biomarker With Pattern Recognition and Machine Learning Lukas Nolte
14.12.2022 KS2/Geb.F - SR-III 10:30 h Machine Learning for Optimization Problems and its Application: A Real Time Energy Management for EV Charging Station Tailei Wang
14.12.2022 KS2/Geb.F - SR-III 10:45 h Usage of Convolutional Auto-Encoder Networks in Image Proccessing Agit Culcu
14.12.2022 Online (Zoom) 11:00 h Handwriting Recognition using the SFR model Firdous Bin Ismail
Tbd KS2/Geb.F - SR-III Tbd Image Segmentation with Keras Ralf Burgardt und Hannes Dreier



Current evaluation Completed evaluations