Digital Signal Processing. Prof. Dietrich Klakow Rahil Mahdian

Transcription

1 Digital Signal Processing Prof. Dietrich Klakow Rahil Mahdian

2 Language Teaching: English Questions: English (or German) Slides: English Tutorials: one English and one German group Exercise sheets: most likely English Your solutions: English or German Exam sheet: both languages Your answers: English or German 2

3 Course-Homepage See: www. lsv. uni-saarland. De Contains: - Slides - Exercises - Literature 3

4 Register before Tuesday noon, please! 4

5 Use your university ! Register by Tuesday12:00! 5

6 Lecture Lecture: Monday 10:15-11:45 (12) Location: in building 13 Contact: Rahil Mahdian: tel Termin OK? 6

7 Exercises Exercises: Time: by arrangement (doodle) Location: will be announced on mailing list Tutors: Ali Kanso Merlin Köhler 7

8 Exercises/Practicals Details will follow Two groups Time and date: by doodle Programming language: Matlab (recommended!) or Coordinate with us before you proceed. 8

9 Mailing List We will set a mailing list All students + ME, AK, and DK will be on it Purpose: Raise questions to everybody Discuss questions Announcements (!!! e.g. exam dates!!!) everything else For changes contact: (I will tell you later) Dietmar.Kuhn@lsv.uni-saarland.de 9

10 Exam Written exam 120 Minutes Date: doodle Note: CuK master students can only take it as a core course if it was not mandatory in their bachelors program 10

11 Prerequisites Some math Programming E.g. matlab,c++, java, maple,... 11

12 Rules of the Game In case you don't understand something: 1. Ask!!! 2. Ask!!! 3. Ask!!! 12

13 1. Introduction

14 Outline of the Lecture 1. Introduction: (3 lectures) i. DSP basics, Sampling, Convolution, Windowing, etc. ii. Filtering (e.g., FIR, IIR, etc) 2. Signal representations, analysis, and modeling: (3/4 lectures) i. Transforms: DFS, DTFT, DFT, FFT ii. Multi-resolution Analysis: T-F (Gabor), FilterBank, Wavelet iii. Sparse Representations: DCT, Wavelet iv. Signal modeling: LP, AR, ARMA, etc 3. Microphone Arrays (e.g., multi-sensor processing) 1 lecture 4. [Filtering and Smoothing] : (1-lecture) 5. Feature Extraction (Speech, Image) (1 lecture) 6. PCA (KLT), LDA, ICA/IVA (MIMO), etc (2 lectures) 7. Statistical (& Adaptive) Signal Processing: (2/3 lectures) i. Wiener filter ii. Spectral Subtraction iii. LMS, RLS, Kalman filter 14

15 DSP The main goal A generic term for some techniques: e.g., a. Filtering b. Spectrum analysis c. Compression d. Separation e. etc applied to digitally sampled signals. 15

16 Signal- Analog and Digital 16

17 Image Signal Taken from prof.skatsaggelos, NWU 17

18 Image Signal- resolution Taken from prof.skatsaggelos, NWU 18

19 Signal- Quantization Taken from prof.skatsaggelos, NWU 19

20 Multi-Spectral Imaging Taken from prof.skatsaggelos, NWU 20

21 Multi-Spectral Imaging Taken from prof.skatsaggelos, NWU 21

22 Feature Extraction from Images Main features: Color Texture Edges 22

23 Speech Signal - representations 23

24 Feature Extraction from Speech Standard feature extraction from speech: Mel-Frequency- Cepstral Coefficients 24

25 Intersection of DSP and scientific areas 25

26 Multi-sensor signal processing Beamforming Blind Source Separation 26

27 Microphone Arrays d + τ= d c sinθ Use time delay to enhance signal from a certain direction. 27

28 Spatial Filtering - Beamforming 28

29 Basic Principle of Pattern Recognition Test Data x i Training Data Feature Extraction Feature Extraction Classifier Model Training Algorithm 1 2. n 29

30 Musical Genre Classification Classical? Country Rock 30

31 or Speaker Recognition Speaker verification: is this Mary? Speaker identification: who is speaking? 31

32 or Classification A simple introduction Nearest Neighbor Classifier 32

33 KL-Transform and Linear Discriminant Analysis Find the optimal subspace for feature vectors 33

34 Linear Predictive Coding Basic algorithm for speech coding 34

35 Linear Filters With saltand-pepper noise Gaussian 3x3-kernel blurring of the image use other filters (e.g. median filter) 35

36 Spectral Subtraction and Wiener Filter Suppress noise From: 36

37 Literature Applied Pattern Recognition von Dietrich W. R. Paulus, Joachim Hornegger Vieweg ISBN: Ca. 40 Euro Speech and image analysis Software oriented Signal processing 37

38 Literature Digitale Sprachsignalverarbeitung by Peter Vary, Ulrich Heute, Wolfgang Hess Teubner Verlag ISBN: Ca. 45 Euro Spectral subtraction Wiener Filter Microphone arrays 38

39 Literature Spoken Language Processing by Xuedong Huang, Alex Acero, Hsiao-Wuen Hon, Xuedong Huang, Hsiao-Wuen Hon Prentice Hall ISBN: very comprehensive 39

40 Questionnaire Take a sheet of paper Please give me you opinion of those questions: All topic covered that you expect? Is there a topic we could skip? What do you expect in terms of teaching style What should not happen in the lecture Take yourself 5 minutes time 40