Machine Learning for Speech and Audio Processing

Lecturer: Prof. Dr.-Ing. Peter Jax

Contact: Lars Thieling, Erik Fleischhauer

Type: Master lecture

Credits: 4

Registration via RWTHonline

Course language: English

Material:
Lecture slides and Exercise problems will be published in RWTHmoodle.

Dates

Lecture:

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

Exercise:

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

Consultation Hours:

Individual appointments by arrangement with Lars Thieling

Exam

Monday, 4 March 2024
Appointments upon agreement

The examination takes place in oral form. Please contact Simone Sedgwick promptly to coordinate the appointment.

Resources: For preparation for the oral examination, a handwritten DIN A4 sheet and a non-programmable calculator are allowed.

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
    • 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.

Evaluation

The results of the evaluation are summarized below.

Summer Term 2022

Participants of the evaluation 5

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

MLSAP_Vorlesung_SS22.pdf