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Lecture "Pattern Recognition and Machine Learning"


Basic Information

Lecture overview  
  Lecturers:   Gerhard Schmidt (lecture) and Tobias Hübschen, Bastian Kaulen (exercise)  
  Room:   Zoom  
  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
    • Codebooks
    • Gaussian mixture models (GMMs)
    • Artificial Neural Networks (ANNs)
    • Hidden Markov models (HMMs)
  • Selected applications of machine learning



A preliminary lecture and exercise schedule for WS 20/21 is now available. The lecture will be conducted as a live Zoom meeting, scheduled every Tuesday, 13:00 h. The first lecture starts on November, 3rd. The exercise will be provided as on-demand videos. There will also be two Zoom sessions for the excercise where students can ask questions regarding both the lecture and the exercise.

The Zoom link for the question time on 26.01. was distributed via email on 20.01.21. Please send in questions to This email address is being protected from spambots. You need JavaScript enabled to view it..

Please sign up for the student talks until 24.01.21. Giving the talk is a requirement to sit the exam.

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 13:00 h - 15:30 h.

Date   Lecture   Exercise  
  03.11.2020   Introduction   -  
  10.11.2020   Noise Suppression + Beamforming   Noise Suppression (video)  
  17.11.2020   Beamforming + Feature Extractiom   Beamforming (video)  
  24.11.2020   Feature Extraction + Codebook Training   Feature Extraction (video)  
  01.12.2020   Codebook Training + Bandwidth Extension   Codebook Training (video)  
  08.12.2020   Bandwidth Extension (until 15:00 h)   Zoom Question Time (starts 15:15 h, topics up to Codebook Training) + Bandwidth Extension (video)  
  15.12.2020   Gaussian Mixture Models   Gaussian Mixture Models (video)  
  05.01.2021   Neural Networks   Neural Networks (video)  
  12.01.2021   Hidden Markov Models   Hidden Markov Models (video)  
  19.01.2021   Speaker and Speech Recognition   Speaker and Speech Recognition (video)  
  26.01.2021   -   Zoom Question Time (starts 13:00 h, all topics)  
  02.02.2021   Student Talks   Student talks  
  09.02.2021   Student Talks   Student talks  


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 "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

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



The exercise will consist of two parts using different media. For most lecture topics an individual exercise video will be uploaded. These videos can be watched on demand. Some additional course material will be provided alongside these videos.

The on-demand videos will be supported with two live zoom sessions. Here, students will be able to ask topic-related questions. Students are also encouraged to submit their questions ahead of time so the answers can be supported with slides or other material.

Videos will go live and zoom sessions will be conducted according to the (preliminary) schedule above.

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




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. The talk registration deadline is 24.01.2021.

Below you can find the preliminary schedule of the talks.

Date   Room   Time   Topic   Presenter(s)  
02.02.2021   Zoom   13:05 h   Neural Networks and Reservoir Computing   Ahmad Mahmoud, Moath Al Mobaideen  
02.02.2021   Zoom   13:30 h   Face Recognition   Solveig Baschin  
02.02.2021   Zoom   13:45 h   Handwriting Recognition   Alexandr Langolf  
02.02.2021   Zoom   14:00 h   Automotive Radars   Gladson Nadar  
02.02.2021   Zoom   14:15 h   Support Vector Machines   Lucas Pöhler  
02.02.2021   Zoom   14:40 h   Target tracking using Gaussian Mixture Models   Karolin Krüger, Christian Kanarski  
02.02.2021   Zoom   15:05 h   Empirical and Dynamical Mode Decomposition   Lennart Rolschewski  
02.02.2021   Zoom   15:20 h   K-Means Algorithm and Applications   Rashid Obaid, Usama Adeel  
02.02.2021   Zoom   15:45 h   Concept Drift   Mariusz Szupka, Daniel Krauel  
02.02.2021   Zoom   16:10 h   Image Recognition using CNNs   Şevval Buse Tokmak  
09.02.2021   Zoom   13:00 h   Nonintrusive Load Monitoring   Samir Karim  
09.02.2021   Zoom   13:15 h   Sentiment Analysis   Lukas Elsner  
09.02.2021   Zoom   13:30 h   Probabilistic Language Models   Tobias Meier  
09.02.2021   Zoom   13:45 h   Long Short-Term-Memory (LSTM)   Silas Oettinghaus  
09.02.2021   Zoom   14:00 h   Speech Enhancement with Codebooks   Dennis Zdetski  
09.02.2021   Zoom   14:15 h   Reinforcement Learning   Jonas Olschewski  
09.02.2021   Zoom   14:40 h   Image Colorization   Eike Krebs  
09.02.2021   Zoom   14:55 h   Depth-Aware Video Frame Interpolation   Fabian Kuhnert  
09.02.2021   Zoom   15:10 h   Neural Supersampling   Daniel Bruhn  
09.02.2021   Zoom   15:25 h   Neural networks in Computer Games   Fatih Corumluoglu  





Link   Content   Link   Content  
    Current evaluation     Completed evaluations