Course detail

Signals 2

FEKT-BPC-SI2Acad. year: 2022/2023

The course is focused on analysis and digital processing of signals. It provides a theoretical basis in the areas of random signals, discrete filtering, pattern recognition and speech signal processing. Computer exercises, where the MATLAB software environment is used, contribute to the deepening and verification of theoretical knowledge.

 

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Learning outcomes of the course unit

The graduate of the course is familiar with deterministic and random signals, can perform signal filtering and understands voice technologies. The graduate is also able to solve practical tasks, i.e. choose and justify a suitable method and apply it.

 

Prerequisites

Courses BPC-SI1, BPC-PP1, BPC-MA1, BPC-MA2 are required. Knowledge of the basics of systems and signal theory, mathematics at the bachelor's level and MATLAB.

 

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods include lectures and computer exercises. Student develops individual tasks during computer exercises. The course takes advantage of e-learning system (Moodle).



Assesment methods and criteria linked to learning outcomes

The conditions for successful completion of the course are specified in the annually updated announcement of the guarantor. The score is usually as follows:

- written test focused on counting examples max. 10 points,

- tasks in computer exercises max. 20 points,

- final exam max. 70 points.


Course curriculum

1. Random signals, processes and their characteristics

2. Spectral parameters, windowing functions

3. Discrete linear systems

4. Linear signal filtering. FIR filters

5. IIR filters, designs of digital filters

6. Signal representation and classification of real phenomena

7. Transformation and optimization of signal features

8. Linear prediction of signals

9. Machinery speech recognition

10. Identification of persons by voice

11. Time transformations, time axis warping

12. Cepstral analysis of speech signals

13. Voice analysis for security purposes

 

Work placements

Not applicable.

Aims

The aim of the course is to provide students with theoretical knowledge in the field of digital processing and signal analysis and practical verification of acquired skills.

Specification of controlled education, way of implementation and compensation for absences

All exercises are mandatory. Missed exercises must be made up by the end of the semester.


Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

JAN, J. Číslicová filtrace, analýza a restaurace signálů. 2. upravené a rozšířené vydání. Brno: VUTIUM, 2002. ISBN 80-214-2911-9. (CS)
TAN, L., JIANG, J. Digital Signal Processing: Fundamentals and Applications. New York: Academic Press, 2018. ISBN 978-0-12-815071-9 (EN)

Recommended reading

PSUTKA, J., MÜLLER, L., MATOUŠEK, J., RADOVÁ, V. Mluvíme s počítačem česky. Praha: Academia, 2006. ISBN 80-200-1309-0 (CS)
KOZUMPLÍK, J., JAN, J., KOLÁŘ, R. Číslicové zpracování signálů v prostředí Matlab. Brno: VUT, 2001, 72 s. ISBN 80-214-1964-4 (CS)

eLearning

Classification of course in study plans

  • Programme BPC-EKT Bachelor's, 2. year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

1. Random signals, processes and their characteristics

2. Spectral parameters, windowing functions

3. Discrete linear systems

4. Linear signal filtering. FIR filters

5. IIR filters, designs of digital filters

6. Signal representation and classification of real phenomena

7. Transformation and optimization of signal features

8. Linear prediction of signals

9. Machinery speech recognition

10. Identification of persons by voice

11. Time transformations, time axis warping

12. Cepstral analysis of speech signals

13. Voice analysis for security purposes


Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer

Syllabus


eLearning