Machine Learning for Speech and Audio Processing
Lecturer: Prof. Dr.-Ing. Peter Jax
Contact: Lars Thieling, Jörn Erik Fleischhauer
Type: Master lecture
Credits: 4
Registration via RWTHonline
Course language: English
Material:
Lecture slides and Exercise problems will be published in RWTHmoodle.
Exam
The exam is held orally on Wednesday, 3/1/2023, in the IKS. Dates are given by arrangement. Please contact Simone Sedgwick. (sedgwick(at)iks.rwth-aachen.de)
Please note: Please bring along your student ID (BlueCard)!
The lecture "Machine Learning for Speech and Audio Processing (MLSAP)" addresses especially students of the Master's program "Electrical Engineering, Information Technology and Computer Engineering". The formal connection to the module catalogs can be found at RWTHonline.
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.