Machine Learning for Speech and Audio Processing (New!)

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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

MLSAP_Vorlesung_Uebung_SS18.pdf