13E053SAS - Spectral Signal Analysis

Course specification
Course title Spectral Signal Analysis
Acronym 13E053SAS
Study programme Electrical Engineering and Computing
Module Signals and Systems
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB 6.0 Status elective
Condition Digital signal processing, Stochastic systems and estimation
The goal Introduce students to basics methods for time-series analysis and spectral estimation. Enable students to practically implement and interpret the results of these algorithms using Matlab/Octave and Python.
The outcome Students will understand theoretical and practical aspects of classical and parametric algorithms for estimating power spectra of wide-sense stationary stochastic signals. Students will be enabled to properly choose, practically implement and adequately tune the spectral estimation algorithms, and to interpret the results obtained by applying these methods to realistic signals.
URL to the subject page
URL to lectures
Contents of lectures Stochastic processes. Classical methods for spectral analysis: periodogram, Blackman-Tukey method. Parametric time-series modelling: AR, MA and ARMA models, linear prediction. Spectral estimation of AR models: Yule-Walker equations, Levinson-Durbin recursion, lattice filters, autocorrelation/(modified) covariance/Burg methods. Effect of measurement noise. Model order selection.
Contents of exercises In class, with the teachers supervision and aid, the students will implement and verify methods covered in lectures. As a homework assignment, each student will be given samples of a signal, and their assignment will be to individually estimate and analyse the spectrum of the sampled signal.
  1. Steven Kay, "Modern Spectral Estimation: Theory and Application", Prentice Hall, 1998 (Original title)
  2. Petre Stoica and Randolph L. Moses, "Spectral analysis of signals", Prentice Hall, 2005 (Original title)
Number of hours per week during the semester/trimester/year
Lectures Exercises OTC Study and Research Other classes
3 1 1
Methods of teaching 45 hours of lectures + 15 hours of auditory exercises + 15 hours of practical exercises with computers
Knowledge score (maximum points 100)
Pre obligations Points Final exam Points
Activites during lectures 0 Test paper 0
Practical lessons 0 Oral examination 70
Projects 30
Colloquia 0
Seminars 0