Pattern Recognition and Machine Learning

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
Contents:	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)

News

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.

Schedule

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

Exercises

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: 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

Talks

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

Evaluation

Evaluation
	Current evaluation		Completed evaluations

In the first week of September, we were delighted to host Dr. Isabel Schiller, an early-career researcher from RWTH Aachen University's Work and Engineering Psychology Lab. Isabel is a Clinical Linguist, holds a PhD in Psychology, and is passionate for auditory cognition and voice research. Together, we've embarked on a collaborative endeavor centered around auditory feedback modulation. Our goal is to develop and assess a real-time voice modification system, designed to explore vocal motor control in both healthy speakers and those with voice disorders. As we joined forces this week, we laid the groundwork for the technical implementation of our project and prepared the upcoming launch of our first experiment at RWTH Aachen University. Interdisciplinary collaborations like this one highlight the importance of merging knowledge from different domains, not only for advancing cutting-edge research but also for fostering an enjoyable and enriching experience.