Signal Separation - Independent Components
Signalseparation - oberoende komponenter

BMEN16F, 7.5 högskolepoäng

Gäller från och med: Autumn 2019
Beslutad av: Professor Thomas Johansson
Datum för fastställande: 2019-06-05

Allmänna uppgifter

Avdelning: Biomedical Engineering
Kurstyp: Gemensam kurs, avancerad nivå och forskarnivå
Kursen ges även på avancerad nivå med kurskod: BMEN15
Undervisningsspråk: English

Syfte

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.

Mål

Kunskap och förståelse

För godkänd kurs skall doktoranden

• be able to apply the theory of independent components for modeling of signals and systems
• be able to apply the theory of independent components in the field of signal separation and feature extraction

Färdighet och förmåga

För godkänd kurs skall doktoranden

• have knowledge in problem formulations of modeling of linear mixtures of signals
• have knowledge in the use of independent components in separation of linear mixtures of signals

Värderingsförmåga och förhållningssätt

För godkänd kurs skall doktoranden e able to comprehend literature as well as standards in this area

Kursinnehåll

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.

Kurslitteratur

Hyvärinen, A., Karhunen, J. & Oja, E.: Independent Component Analysis. Wiley-Interscience, 2001. ISBN 9780471405405.

Kursens undervisningsformer

Undervisningsformer: Föreläsningar, övningar, projekt

Kursens examination

Examinationsformer: Skriftlig tentamen, skriftlig rapport, inlämningsuppgifter. Fulfilled project work and partial tests during the course.
Betygsskala: Underkänd, godkänd
Examinator:

Antagningsuppgifter

Förutsatta förkunskaper: ESS040, EITF75 Digital signal processing OR ETI265, EITA50 Signal processing in multimedia OR EITF15 Digital signal processing - theory and applications

Kurstillfällesinformation

Kontaktinformation och övrigt

Kursansvarig: Frida Sandberg <frida.sandberg@bme.lth.se>
Hemsida: www.bme.lth.se