1. Digital Signal Processing Rahil Mahdian22.03.2016 LSV Lab, Saarland University, Germany

2. 2 Course-Homepage See: https://www.lsv.uni- saarland.de/index.php?id=137 Contains: -Slides -Exercises -Additional Notes -Literature -Announcements -Sample problems Email: mahdian.t.r@gmail.commahdian.t.r@gmail.com LSV Lab, Saarland University, Germany

3. 3 Lectures & Timeline (Schedule)

4. 4 Exercises/Practicals Details will follow Groups of two people => preferred Final Project : extra point you can choose now Programming language: –Matlab (recommended!) –or Coordinate with us before you proceed. LSV Lab, Saarland University, Germany

5. 5 Exam Written exam 120 Minutes Date: May, 2nd (15:00 - 17:00) Note: CuK master students can only take it as a core course if it was not mandatory in their bachelors program LSV Lab, Saarland University, Germany

6. 6 Prerequisites Some math (Calculus, Linear Algebra, Probabilities & Statistics) Programming E.g. matlab,C++,... LSV Lab, Saarland University, Germany

7. 1. DSP Theory Basics Part-1 (Motivation, Applications, Categories) LSV Lab, Saarland University, Germany

8. 8 DSP – The main goal A generic term for some techniques: e.g., a.Filtering (e.g., LP, AR, IIR, FIR, etc.) b.Analysis (e.g., Spectrum, MRA, wavelet, etc.) c.Compression (e.g., PCA, JPEG, etc.) d.Synthesis (e.g., LPC, HMM, etc.) e.Separation (e.g., ICA, IVA, NMF, CCA, etc.) f.Learning (e.g. GMM, HMM, DicL, etc.) g.Etc. applied to digitally sampled signals. LSV Lab, Saarland University, Germany

9. 9 DSP – Some Applications LSV Lab, Saarland University, Germany

10. 10 Signal- Analog and Digital

11. 11 Speech Signal - representations LSV Lab, Saarland University, Germany

12. 12 Feature Extraction from Speech Standard feature extraction from speech: Mel-Frequency- Cepstral Coefficients LSV Lab, Saarland University, Germany

13. 13 Image Signal Taken from prof.sKatsaggelos, NWU

14. 14 Image Signal- resolution Taken from prof.sKatsaggelos, NWU

15. 15 Signal- Quantization Taken from prof.sKatsaggelos, NWU

16. 16 Multi-Spectral Imaging Taken from prof.sKatsaggelos, NWU

17. 17 Multi-Spectral Imaging Taken from prof.sKatsaggelos, NWU

18. 18 Feature Extraction from Images Main features: Color Texture Edges LSV Lab, Saarland University, Germany

19. 19 Intersection of DSP and scientific areas LSV Lab, Saarland University, Germany

20. 20 Beamforming Blind Source Separation Multi-sensor signal processing

21. 21 Microphone Arrays + d Wave front  Use time delay to enhance signal from a certain direction. LSV Lab, Saarland University, Germany

22. 22 Spatial Filtering - Beamforming LSV Lab, Saarland University, Germany

23. 23 Test Data x i Basic Principle of Pattern Recognition Feature Extraction Classifier Model 11 …. Feature Extraction Training Data Training Algorithm 22 nn LSV Lab, Saarland University, Germany

24. 24 Musical Genre Classification ? ? ? Classical Country Rock LSV Lab, Saarland University, Germany

25. 25 or Speaker Recognition Speaker verification: is this Mary? Speaker identification: who is speaking? LSV Lab, Saarland University, Germany

26. 26 or Classification A simple introduction Nearest Neighbor Classifier LSV Lab, Saarland University, Germany

27. 27 Algorithms- KL Transform and Linear Discriminant Analysis Find the optimal subspace for feature vectors LSV Lab, Saarland University, Germany

28. 28 Analysis - Linear Predictive Coding Basic algorithm for speech coding LSV Lab, Saarland University, Germany

29. 29 Linear Filters With “salt- and-pepper” noise Gaussian 3x3-kernel  blurring of the image  use other filters (e.g. median filter) LSV Lab, Saarland University, Germany

30. 30 Spectral Subtraction and Wiener Filter From: http://www.tu-harburg.de/ft2/AktuForschungen/Bildverarbeitung/Bildverarbeitung.htm Suppress noise

31. 31 Literature Applied Pattern Recognition von Dietrich W. R. Paulus, Joachim HorneggerDietrich W. R. PaulusJoachim Hornegger Discrete-Time Signal Processing by Alan V. Oppenheim et al. Spoken Language Processing by Xuedong Huang, Alex Acero, et al. Bayesian Reasoning and ML By David Barber

32. 32 Determnistic Random Deterministic vs. Random Signals LSV Lab, Saarland University, Germany

33. 33 Non-Causal Anti-Causal Causal Causal Signals LSV Lab, Saarland University, Germany

34. 34 Discrete time vs. Digitized Signal LSV Lab, Saarland University, Germany

35. 35 Quantized signal input output

36. 36 # of Bits Assigned LSV Lab, Saarland University, Germany

37. 37 Analog to Digital and Vice Versa LSV Lab, Saarland University, Germany

38. 38 Multi-Resolution vs. Single resolution LSV Lab, Saarland University, Germany

39. 39