Course syllabus

Signalseparation - oberoende komponenter
Signal Separation - Independent Components

EITN55, 7,5 credits, A (Second Cycle)

Valid for: 2015/16
Decided by: Education Board A
Date of Decision: 2015-04-10

General Information

Elective for: BME5-sbh, C4-ssr, D4-ssr, E4-ss, E4-mt, E4-bg, F4, F4-ss, MWIR2, Pi4
Language of instruction: The course will be given in English on demand


The course gives basic knowledge in statistical signal processing and treats the theory of independent and principal components, together with applications in signal separation. The traditional approaches to analyse, filter, compress and separate a combination of signals by means of second order statistics (e.g. correlation based methods) are extended to include higher order statistics (e.g. higher than second order moments). This leads to the concept of independent components in contrast to principal components.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

Competences and skills
For a passing grade the student must



Judgement and approach
For a passing grade the student must


The following items are treated in the course: linear representation of multivariate data, random vectors and independence, higher order moments, gradients and optimization, learning rules for non-constrained and constrained optimization, estimation theory for signal separation, methods of least-squares and maximum likelihood, information theory, entropy cumulants, definition of PCA and ICA, differences and similarities between PCA and ICA, methods for estimation of ICA: ICA by maximization of non-Gaussianity, ICA by maximum likelihood estimation, ICA by minimization of mutual information, ICA by nonlinear decorrelation and nonlinear PCA. Applications: acoustic signal separation and deconvolution, feature extraction from multivariate data, artifact identification from EEG and MEG, prediction of time series data by using ICA.

Examination details

Grading scale: TH
Assessment: Written exam, fulfilled laboratory work and partial tests during the course.

Code: 0114. Name: Signal Separation - Independent Components.
Credits: 7,5. Grading scale: TH. Assessment: Written exam
Code: 0214. Name: Laboratory Work 1.
Credits: 0. Grading scale: UG.
Code: 0314. Name: Laboratory Work 2.
Credits: 0. Grading scale: UG.


Required prior knowledge: ESS040 Digital signal processing OR ETI265 Signal processing in multimedia OR EITF15 Digital signal processing - theory and applications
The number of participants is limited to: No

Reading list

Contact and other information

Course coordinator: Nedelko Grbic,
Course homepage: