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 23/24 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
25.10.2023 Introduction -
01.11.2023 Noise Suppression + Beamforming Noise Suppression (video)
08.11.2023 Beamforming + Feature Extraction Beamforming (video)
15.11.2023 Feature Extraction + Codebook Training Feature Extraction (video)
22.11.2023 Codebook Training + Bandwidth Extension Codebook Training (video)
29.11.2023 Bandwidth Extension Bandwidth Extension (video)
06.12.2023 Gaussian Mixture Models Gaussian Mixture Models (video)
13.12.2023 Student Talks Student talks
10.01.2023 Neural Networks -
17.01.2024 Neural Networks Neural Networks (video)
24.01.2024 Hidden Markov Models Hidden Markov Models (video)
31.01.2024 Explainable artificial intelligence Speaker and Speech Recognition (video)


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. There will be an in-presence exercises 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 6.12.2023.

Date Room Time Topic Presenter(s)
13.12.2023 KS2/Geb.F - SR-III 08:15 h Opening Gerhard Schmidt
13.12.2023 KS2/Geb.F - SR-III 08:25 h Machine-learning-based ECG Analysis Maureen Petersilka
13.12.2023 KS2/Geb.F - SR-III 08:40 h Machine Learning for Optical Communication Systems Henning Eikens
13.12.2023 KS2/Geb.F - SR-III 08:55 h Edge Computing for Artificial Intelligence (Edge-AI) Daniel Wittmann
13.12.2023 KS2/Geb.F - SR-III 09:10 h Spiking Neural Networks Tilman Müller
13.12.2023 KS2/Geb.F - SR-III 09:25 h Gaussian Splatting Alberto Rodriguez Botejara
13.12.2023 KS2/Geb.F - SR-III 09:40 h Marine Autopilot Applications Jonas Huwer



Current evaluation Completed evaluations