Signal Processing

Course dates – MT – week beginning Monday 15th October 2018 – for Year 1 students
Xiaowen Dong

  • Provide an overview of classical signal processing concepts and tools
  • Provide an overview of filters and stochastic models
  • Provide an overview of signal representations via transforms and dictionaries
  • Present the concepts and practice of the new field of signal processing on graphs


  • LTI systems, convolution, time-/frequency-domain analysis, filters
  • Sampling, digital filters, z-transform, frequency response of digital filters
  • Design of digital filters
  • Spectral estimation, discrete Fourier transform, stochastic models
  • Time-frequency representation, Fourier and wavelet transforms
  • Dictionary learning
  • Introduction to signal processing on graphs
  • Representation and learning of signals on graphs
  • Application I: Learning graphs from data
  • Application II: Deep learning on graphs


  • Linear algebra
  • Probability and statistics
  • MATLAB and Python
Other Sources


  • An introduction to the analysis and processing of signals. Macmillan, 1989.
  • Oppenhein and Shafer. Digital signal processing. Prentice Hall, 1975.
  • Proakis and Manolakis. Digital signal processing: Principles, algorithms and applications. Prentice Hall, 2007.
  • Introduction to signal processing. Prentice Hall, 1996.
  • Vetterli et al. Foundations of Signal Processing. Cambridge University Press, 2014.
  • Kovačević et al. Fourier and wavelet signal processing. Online at


  • Rubinstein et al., “Dictionaries for sparse representation modeling,” Proceedings of the IEEE, 2010.
  • Shuman et al., "The emerging field of signal processing on graphs," IEEE Signal Processing Magazine, 2013.
  • Dong et al., “Learning graphs from data,” arXiv, 2018.
  • Bronstein et al., “Geometric deep learning,” IEEE Signal Processing Magazine, 2017.
  • Coding methods and testing on real world data
Assessment Mode