Content, progress and pedagogy of the module

Objectives

Modern multimedia systems most often make use of numerous sensors to capture inputs from the user(s) for interaction with the system. However, the real-life environment around such systems is typically dynamic, noisy, and unpredictable, which hinders direct application of data-driven methods to infer the desired user interaction. This course therefore introduces signal processing theory and methods for analyzing and processing sensor data, e.g., to facilitate robust feature extraction and data clean-up for subsequent machine learning. More specifically, the course will go over the following topics: spectral analysis, signal modelling, parameter estimation, noise estimation, signal enhancement, and multichannel filtering.

Learning objectives

Knowledge

Students who complete the module will obtain:

• understanding of basic spectral analysis methods, including both non-parametric and parametric methods
• understanding of basic signal models for describing sensor data and how to extract their parameters from sensor data
• understanding of the fundamentals of signal enhancement, e.g., noise estimation, optimal filtering, and multi-sensor filtering

Skills

Students who complete the module will be able to: 

• choose and implement appropriate methods for robust extraction of features from sensor data (e.g., IMU samples, audio recordings, images)
• choose and implement methods for reducing noise components in sensor data
• apply knowledge to compare different signal processing methods for interactive systems in terms of computational efficiency and accuracy

Competences

Students who complete the module will be able to:

• analyze a data set related to interactive systems and identify appropriate methods for processing the data, e.g., to enable machine learning for interaction
• apply the relevant signal processing methods to an interactive systems data set and evaluate and discuss the results and their implications on the system

Type of instruction

Refer to the overview of instruction types listed in § 17.

Exam

Prerequisite for enrollment for the exam

• To be eligible to take the exam, the student must timely have handed in any mandatory assignments

Exams