Machine Learning for Speech and Audio Processing (New!)
Sie verwenden einen Browser, in dem JavaScript deaktiviert ist. Dadurch wird verhindert, dass Sie die volle Funktionalität dieser Webseite nutzen können. Zur Navigation müssen Sie daher die Sitemap nutzen.
You are currently using a browser with deactivated JavaScript. There you can't use all the features of this website. In order to navigate the site, please use the Sitemap .
Lecturer: Prof. Dr.-Ing. Peter Jax
Contact: Maximilian Kentgens
Type: BLO/ZUS (V2Ü1)
Credits: 4
Campus Course
Learning room L2P
(Registration via Simone Sedgwick)
Course language: English
Material:
Lecture notes and exercise problems will be published ahead of each lecture and exercise.
Dates
Lecture
from Friday, April 13, 2018
08:30 - 10:00
Lecture room 4G
Übung:
from Friday, April 13, 2018
10:15 - 11:00
Lecture room 4G
Consultation hours:
If necessary, please contact Maximilian Kentgens by stating the topic.
Exam
The exam is held orally on 25.07., 30.08., 11.09. and 26.09. Dates are given by arrangement. Please contact Simone Sedgwick.
Resources: Written resources (e.g., lecture notes or books) are not permitted.
Please note: Please bring along your student ID (BlueCard)!
The new lecture "Machine Learning for Speech and Audio Processing" addresses especially students of the Master's programme "Electrical Engineering, Information Technology and Computer Engineering" in the fields of "Communications Engineering" and "Technical Informatics". In the summer semester 2018 it is an ungraded examination and is recognized as an additional qualification "BLO/ZUS".
In summer term 2019, curricular anchoring will be provided as an elective module in the specialisations "Communications Engineering" and "Technical Informatics".
Content
In this one term lecture the fundamental methods of machine learning with applications to problems in speech and audio signal processing are presented:
- Fundamentals of Classification and Estimation
- Basic Problems of Classification
- Feature Extraction Techniques
- Basic Classification Schemes
- Probabilistic Models
- Stochastic Processes and Models
- Gaussian Mixture Models (GMMs)
- Hidden Markov Models (HMMs)
- Training Methods
- Bayesian Probability Theory: Classification and Estimation
- Particle Filter
- Non-Negative Matrix Factorization (NMF)
- Dictionary-based concept
- Neural Network and Deep Learning
- Feed-Forward Neural Networks
- Fundamental Applications
- Learning Strategies: Supervised vs Unsupervised vs Reinforcement Learning
- Training of Synaptic Weights: Backpropagation and Stochastic Gradient Descent
- Behavior of Learning and the “Magic” of Setting Hyper‐Parameters
- Generative Networks as a Complement to Directed Graphs
- From „Shallow“ to „Deep“: Trade Comprehensibility for Performance
- Specific Network Architectures
- Applications in Signal Processsing
- Interpretations and Realizations
Exercises are offered to gain a deeper understanding on the basis of practical examples.
Evaluation
The results of the evaluation are summarized below.
Summer term 2018
Participants of the evaluation: 12
Global grade: 1,3
Lecture
Concept of the lecture: 1,2
Instruction and behaviour: 1,3
Exercise:
Concept of the exercise: 1,5
Instruction and behaviour: 1,3