Signal Processing (ESPRO)

**Contents:**

- Meeting times and locations
- Teaching staff and contact info
- Course description
- Marks and grading
- Lecture
- Examination tasks
- Textbooks and suggested readings
- Tutorial (incl. Homework)

Monday, 12:15-14:00 (i.e. 0:15 p.m.- 2:00 p.m.), room 528, E&IT Faculty

Tutorial:

Monday (every second), 14:15-16:00, lab. P113, E&IT Faculty

Office: room 565, E&IT Faculty, Institute of Control and Computation Eng.

Office hours: Tuesday, 12.15-14.00

Phone: +22 234 7866

E-mail: W.Kasprzak (at) elka.pw.edu.pl

Maciej Stefańczyk, M.Sc.

Office: room 564

Office hours:

Phone:

Email: M.Stefanczyk (at) elka.pw.edu.pl

This course provides an introduction to digital signal processing from the perspective of audio and speech signals. Following parts are distinguished: signals and systems, Fourier Transform, digital filters, transforms and speech analysis.

**Topics**

Analog and digital signal conversion.
Linear systems. Common signal decompositions.
Convolution – its principle, properties and impulse responses. Correlation.
Real and complex Fourier Transform.
Applications of Fourier transform - spectral analysis of signals, frequency response of systems.
Fourier transform properties. Discrete Fourier transform.
Fast Fourier transform. Moving average filters. Windowed-sinc filters.
Deconvolution and optimal filters. Recursive filters. Chebyshev filters.
The Laplace transform and z-transform. Speech analysis.

**Prerequisities**

Students are expected to have the following background:

- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program, preferably in Matlab.
- Familiarity with basic mathematical analysis and probability theory.

**Course materials**

Lecture notes will be posted periodically on the course web site.
Selected chapters from the books below are recommended as optional reading.

ECTS Grade | A | B | C | D | E | F/FX |

mark | 100- 91 | 90-81 | 80-71 | 70-61 | 60-51 | 50 or less |

- tutorial assessment (up to 40 pts.),
- tests (2 parts) (0-60 pts.).

__Lecture schedule (tentative):__

__Signals and systems__

Week 1 [8.10] 1. Signals (statistics, probability and noise, analog and digital signals) SP-1

Week 2 [15.10 postponed] 2. Systems (linear systems, common signal decompositions) SP-2

Week 3 [22.10] 3. Convolution (principle, properties, common impulse responses, correlation) SP-3

__Fourier transform__

Week 4 [29.10] 4. Discrete Fourier Transform. Real DFT. SP-4

Week 5 [5.11->19.11] 5. Fourier transform properties. SP-5

Week 6,7 [12.11->26.11, 3.12] 6. Complex Fourier Transform. FFT. SP-6**Week 8 [19.11-> 10.12] TEST 1**

__Digital filters__

Week 9 [3.12 -> 17.12] 7. Digital filters. FIR filters. SP-7

Week 10 [2.01.19, Wednesday] 8. Custom filters. SP-8

Week 11 [17.12, 7.01] 9. Recursive filters. SP-9

__Transforms__

Week 12 [14.01] 10. The Laplace transform. SP-10

Week 13 [21.01] 11. The z-transform. SP-11Week 14

**[28.01, time 12.15, room 528] TEST 2**Week 15

**[31.01, time 12.15, room 528] RETAKE TEST**

- Part I (sec. 1-6): Test 1 examples
- Part II (sec. 7-11): Test 2 examples

- [1] Steven W. Smith.
*The Scientist and Engineer's Guide to Digital Signal Processing*. Second Edition, California Technical Publishing, San Diego, CA, 1999, on-line: www.dspguide.com.

For each lecture section, one or more suggested readings are given below.

(Week) Topic | Readings (PDF) | |

(Week 1, 2) 1. Signals | Smith-ch2, Smith-ch3 : [Smith, ch. 2,3] | |

(Week 3) 2. Systems | Smith-ch5 [Smith, ch. 5] | |

(Week 4) 3. Convolution | Smith-ch6, Smith-ch7: [Smith, ch.6, 7] | |

(Week 5) 4. DFT | Smith-ch8, Smith-ch9, [Smith, 8,9] | |

(Week 6 ) 5. FT properties | Smith-ch10, Smith-ch11, [Smith, 10, 11] | |

(Week 7) 6. FFT. Complex DFT | Smith-ch12, Smith-ch31, [Smith, 12, 31] | |

(Week 8) TEST 1 | ||

(Week 9) 12. Speech analysis | ||

(Week 10) 7. Digital filters | Smith-ch14, Smith-ch15, Smith-ch16, [Smith, 14,15,16] | |

(Week 11) 8. Custom filters | [Smith, 17,18] | |

(Week 12) 9. Recursive filters | [Smith, 19,20,21] | |

(Week 13) 10. Laplace transform | [Smith, 32] | |

(Week 14) 11. z-transform | [Smith, 33] | |

(Week 15) TEST 2 |

__Tutorial schedule__

- E1 [8.10] : Introduction to Matlab
- E2 [22.10] : Signals
- E3 [5.11] : Systems. Convolution
- E4 [19.11] : Fourier Transform.
- E5 [3.12] : Homework assignment.
- E6 [17.12] :
- E7 [7.01] : Prototype evaluation
- E8 [21.01] : Final project evaluation

Homework topics:

Group I (project 1-3): noise detection and cancellation.

Signal samples - 1glass.zip.

- Project 1 - frequency-based filtering

Notes: SP1-SP8. - Project 2 - noise estimation and subtraction

Notes: SP-12, SP1-SP8. - Project 3 - spectrogram clustering

Notes: SP1-SP8, Introduction on clustering.

Group II (project 4-6): auditory scene analysis.

- Project 4 - DOA and speaker number detection using two microphones

Papers: DUET_2004.pdf, Kasprzak_JTIT2011.pdf.

Signal samples - 2_two_micro_localization.zip(Matlab .mat files). - Project 5 - speaker detection and localization with three microphones

Papers: Hioka2009, Hamada_ETFA2012.pdf.

Signal samples: 2_three_micro_localization.zip;(Matlab .mat files). - Project 6 - WDO analysis of audio signals.

Papers: DUET_2004.pdf, Kasprzak_JTIT2011.pdf.

Audio samples: 2_localization_analysis.zip;(Matlab .mat files).

Group III (project 7-8): audio signal separation in the time domain.

- Project 7 - adaptive blind source separation.

Papers: Introduction to ICA and BSS.

Kasprzak_2000_Ch3.pdf.

Signal samples - 3separation.zip - Project 8 - analysis of source independence.

Papers: Introduction to ICA and BSS.

Kasprzak_2000_Ch3.pdf.

Signal samples - 3separation.zip

Group IV (project 9-11): speech feature detection and analysis.

Signal samples - 4speech.zip

- Project 9 - time-domain speech analysis.

Notes: SP-12. - Project 10 - formant-based speech analysis.

Notes: SP-12.

Papers: Formant normalization and tracking. - Project 11 - MFCC features of speech.

Notes: SP-12, Introduction on clustering.

Papers: Ch. 9 - cepstrum analysis.

Last modification: 27.11.2018