Course detail

Digital Signal Processing

FEKT-LCSIAcad. year: 2010/2011

Characteristics and classification of discrete signals and systems. Operations with signals and examples of systems. Spectral analysis using FFT. Spectrograms and moving spectra. Discrete Hilbert transform. Representation of pass-band signals.Power spectral density and its estimation. Non-parametric methods. Linear prediction analysis. Autoregression processes, moving average. Parametric methods for calculating power spectral density. Adaptive filtering. Type LMS and RLS gradient algorithms. Adaptive block filters. Decimation and interpolation. Transversal and polyphase filters. Banks of filters with perfect reconstrruction. Half-band filters. Wavelet transformation. Signal analysis with multiple resolution. Compression of audio-signals in telecommunications.

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

prof. Ing. Zdeněk Smékal, CSc.

Department

Department of Telecommunications (UTKO)

Learning outcomes of the course unit

The student will be equipped for establishing spectral properties of deterministic and random signals using various base functions (Fourier analysis, wavelets) for multiple resolution. He will know how to use multirate filter banks (applied, for example, in methods of audio- and video-signal compression, ADSL transmission, etc.).

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Requirements for completion of a course are specified by a regulation issued by the lecturer responsible for the course and updated for every.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

The aim of the course is to present modern methods of digital signal processing that are based primarily on parametric and non-parametric spectral analysis, linear prediction and digital signal processing banks of multirate digital filters.

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

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme EEKR-ML Master's

    branch ML-TIT , 1 year of study, summer semester, compulsory

  • Programme EEKR-CZV lifelong learning

    branch EE-FLE , 1 year of study, summer semester, compulsory

Type of course unit

 

Lecture

39 hod., optionally

Teacher / Lecturer

prof. Ing. Zdeněk Smékal, CSc.

Syllabus

Characterization and classification of discrete signals. Operations with signals: filtering, generating complex signals, modulation and demodulation, time and frequency multiplex, etc. Examples of typical signals (speech, seismic signals, ECG and EEG signals, modulated signals, etc.)
Characterization and classification of discrete systems. Compressors and expanders, limiters and equalizers, frequency and time filters, noise reduction, musical effects, tone selection, echo suppression, etc.
FFT-based spectral analysis. Relation between DFT and bank of filters. Windowing in spectral analysis. Calculation of spectrum at a point and on a curve in z-plane. Chirp z-transform algorithm. Spectrograms and moving spectra. Goertzel algorithm.
Discrete Hilbert transform. Analytical signal. Minimum phase condition. Calculation of instantaneous frequency. Representation of limited-band signals.
Power spectral density and its estimation. Consistent estimation. Calculation based on correlation. Periodogram. Non-parametric methods. Bartlet and Welch methods.
Linear prediction analysis. Representation of stationary random process using rational fraction function. Autoregression processes, moving average. Direct and reverse linear prediction. Examples of applications in mobile networks.
Parametric methods for calculating power spectral density. Type AR model (Yule-Walker method). Spectrum estimation with maximum entropy (Burg method). Type ARMA models and estimation of their parameters.
Adaptive filtering. Type LMS and RLS algorithms and their modifications. Adaptive block filters. Application examples (adaptive echo suppression in ADSL transmission, equalization in mobile network, etc.). .
Digital signal processing via sampling frequency change. Decimation and interpolation. Design of multirate digital filters.
Transversal and polyphase filters. Two-channel and multi-channel quadrature mirror filters (QMF).
Filter banks with perfect reconstruction. Half-band filters. Para-unitary filter banks. SBC filter banks. Octave filter banks and wavelets.
Wavelet transform. Signal analysis with multiple resolution. Discrete wavelet transform. Orthogonal and biorthogonal filter banks.
Compression of audio-signals in telecommunications. PCM bit data flow and its reduction. Masking and perceptional coding. Filter banks of compression methods. Type MPEG audio standards.

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

Ing. Ondřej Rášo, Ph.D.

Syllabus

Matlab-based modelling of basic operations with signals.
Basic types of discrete systems and methods of their application.
FFT spectral analysis, calulation of spectrum at a point and on a curve.
Analytical signal and establishment of instantaneous frequency. Minimum phase systems.
Bartlet and Welch methods for caculating power spectral density, using Matlab.
Test No 1.
Linear prediction, modelling of autoregression processes.
Parametric methods of establishing correlation and power spetral density.
Adaptive algorithms and their modelling with the aid of Matlab.
Test No 2.
Decimation and interpolation. Filter banks.
Matlab-modelled compression methods for audio- and video-signals.
Test No 3