Machine Learning for Speech and Audio Processing

Lecturer: Prof. Dr.-Ing. Peter Jax

Contact: Egke Chatzimoustafa, Lars Thieling

Type: Master lecture

Credits: 4

Registration via RWTHonline

Course language: English

Lecture slides and Exercise problems will be published in RWTHmoodle.



from Thursday, April 11, 2024
08:30 - 10:00 AM
Lecture Room FT


from Thursday, April 11, 2024
10:30 - 11:00 AM
Lecture Room FT

Consultation Hours:

Monday, August 12, 2024
2:00 - 3:30 PM

Friday, August 16, 2024
2:00 - 3:00 PM

The Q&A session takes place online. The zoom link will be announced via email.


Monday, August 19, 2024
11:00-12:30 AM
Room Müchener Aula

The exam may be in oral format depending on the number of participants.


Tuesday, October 1, 2024
2:00 PM

The inspection takes place in the IKS, lecture room 4G.

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.


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
    • Bayesian Probability Theory: Classification and Estimation
    • Feature Extraction Techniques
    • Modeling of Statistical Distributions
    • Basic Classification Schemes
  • Probabilistic Models
    • K-Means Clustering
    • Gaussian Mixture Models (GMMs)
    • Expectation-Maximization (EM) Algorithm
  • Modeling Sequential Data
    • Hidden Markov Models (HMMs)
    • Estimation and Classification with HMMs
    • Linear Dynamical Systems (LDS)
  • Non-Negative Matrix Factorization (NMF)
  • Neural Networks and Deep Learning
    • Elements of Neural Networks
    • Feed-Forward Neural Networks
    • Training of Synaptic Weights: Backpropagation and Stochastic Gradient Descent (SGD)
    • Specialized Network Architectures: CNNs, RNNs, LSTMs
    • Advanced Learning Techniques

Exercises are offered to gain a deeper understanding on the basis of practical examples.


The results of the evaluation are summarized below.

Summer Term 2022

Participants of the evaluation 5

Global grade: 1,6
Concept of the lecture: 1,5
Instruction and behaviour: 1,6