Lecture "Pattern Recognition and Machine Learning"

Basic Information

Lecturers:   Gerhard Schmidt (lecture) and Tobias Hübschen (exercise)
Room:   F-SR-I
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
  • Speech and speaker recognition
    • Fundamentals of speech generation
    • Feature extraction
    • Gaussian mixture models (GMMs)
    • Artificial Neural Networks (ANNs)
    • Hidden Markov models (HMMs)
    • Recognition of speech and speakers
  • Enhancement of signal playback
    • Extending the bandwidth of speech signals



Starting this winter semester, the exercise will be extended by practical tasks such as training a neural network using Python and TensorFlow.  For this practical part of the excercise you are recommended to bring your own laptop. The exercise will be conducted as blocks, the schedule can be found below.

Exam appointments have to be acquired offline this semester. The following dates are available:

  • 10.02.2020 (morning + afternoon)
  • 19.02.2020 (morning) -> fully booked
  • 27.03.2020 (morning) -> fully booked

Appointments are available in room D-016. You may sit the exam individually or in groups of two.




The following schedule regarding lectures and excercises is preliminary and may be adapted during the semester. Each date refers to the time slot from 13:00 h until 16:30 h.

Date   Content
22.10.2019   Introduction
29.10.2019   Noise Suppression + Beamforming (part 1)
05.11.2019   Beamforming (part 2) + exercise
12.11.2019   Feature Extraction + Codebooks (part 1)
19.11.2019   Codebooks (part 2) + Bandwidth Extension
26.11.2019   Exercise
03.12.2019   Gaussian Mixture Models + Neural Networks (part 1)
10.12.2019   Neural Networks (part 2) + exercise
17.12.2019   Exercise
07.01.2020   Hidden Markov Models
14.01.2020   Speaker and Speech Recognition + exercise
21.01.2020   Exercise
28.01.2020   Student Talks


Lecture Slides

  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 "Hidden Markov Models (HMMs)"
(Efficient probability calculation, training of HMMs)
  Slides of the lecture "Speaker and Speech Recognition"
(Application of GMMs and HMMs, speech dialog systems)
  Slides of the lecture "Neural networks"
(Network types, training procecures)


Matlab Demos

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



Please note that the questionnaires will be uploaded every week before the excercises, if you download them earlier, you won't get the most recent version.

  Questionnaire for the lecture "Noise Suppression"
  Questionnaire for the lecture "Beamforming"
  Questionnaire for the lecture "Feature extraction"
  Questionnaire for the lecture "Codebook training"
  Questionnaire for the lecture "Bandwidth extension"
  Questionnaire for the lecture "Gaussian Mixture Models"
  Questionnaire for the lecture "Hidden Markov Models"
  Questionnaire for the lecture "Speaker and speech recognition"
  Questionnaire for the lecture "Neural Networks"



At the end of the semester, 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.

Below you can find the schedule of the talks.

Date   Room   Time   Topic   Presenter(s)
28.01.2020   F-SR I   13:05 h   Random Forests   Bastian Schroeter
28.01.2020   F-SR I   13:20 h   Transfer Learning   Kristina Apelt
28.01.2020   F-SR I   13:35 h   Reinforcement Learning   Daniaal Dar
28.01.2020   F-SR I   13:50 h   Transformer Models   Sönke Bartels
28.01.2020   F-SR I   14:05 h   Face Recognition   Yasin Akbaba
28.01.2020   F-SR I   14:30 h   Biological Pattern Recognition   Jannek Winter
28.01.2020   F-SR I   14:45 h   Self Learning   Tim Schmidt
28.01.2020   F-SR I   15:00 h   Vehicular Crash Detection using Hidden Markov Models   Toni Lekic



Programs and Data

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  Current evaluation     Completed evaluations

Website News

17.12.2019: Journal paper on signal processing for breathing protection masks published.

23.11.2019: GaS price 2019 for Jannek Winter for an excellent bachelor topic on underwater communication systems.

15.11.2019: Our new MIMO-SONAR system (sponsored by DFG) is now ready for "take off".

20.10.2019: We had a very good retreat on the island of Sylt.

07.08.2019: Talk from Juan Rafael Orozco-Arroyave added.

11.07.2019: First free KiRAT version released - a game for Parkinson patients

25.06.2019: About 30 pupils from the Isarnwohld-Schule in Gettorf visited us.

02.05.2019: Christin Baasch finished sucessfully her defense on the evaluation of Parkinson speech.

30.11.2018: New student project on driver distraction added.

Recent Publications


M. Brodersen, A. Volmer, G. Schmidt: Signal Enhancement for Communication Systems Used by Firefighter, EURASIP Journal on Audio, Speech, and Music Processing, vol. 21, pp. 1 - 19, 2019


E. Elzenheimer, H. Laufs, W. Schulte-Mattler, G. Schmidt: Signal Modeling and Simulation of Temporal Dispersion and Conduction Block in Motor Nerves, IEEE Transactions on Biomedical Engineering, November 2019, doi: 10.1109/TBME.2019.2954592


Prof. Dr.-Ing. Gerhard Schmidt

E-Mail: gus@tf.uni-kiel.de

Christian-Albrechts-Universität zu Kiel
Faculty of Engineering
Institute for Electrical Engineering and Information Engineering
Digital Signal Processing and System Theory

Kaiserstr. 2
24143 Kiel, Germany

How to find us

Recent News


Recently, we get the new projector front-ends from Atlas Elektronik for our MIMO SONAR system. This was part of the "Großgerät" which was obtained with the help of the German research foundation (DFG). Now we are able to perform underwater experiments that will show if our ideas and corresponding realt-time algorithms also works in real environments.

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