Adaptive Filtering

Lecturer:

Tutor:

Supervisor:

Department of Circuit Theory

Synopsis:

The aim of this subject is to give the overview of adaptive filtering methods and their applications to speech and biological signal processing, noise cancellation, signal separation and signal analysis. Seminars are devoted to the implementation of algorithms in MATLAB. It is highly recommended to be acknowledged with the bases of digital signal processing.

Requirements:

The elaboration and representation of one selected problem is the condition for the credit.

Syllabus of lectures:

1. Batch and recursive signal analysis, basic terms

2. Batch parametric methods, least squares

3. Recursive parametric methods, accuracy of estimation

4. Memory shaping, RLS and LMS algorithms, properties

5. Estimation and signal modelling, RLS/LMS FIR estimator

6. Adaptive line enhancement, conditions, application to speech

7. Analysis of LMS algorithm, error surface, stability, equalization

8. Modifications of LMS algorithm, gradient noise, convergence

9. Narrow-band signal separation, LMS FIR/CIIR, resonator

10. Noise compensation, conditions of proper function

11. Symmetric adaptive structures for sources separation

12. Hyperstabil algorithms, LMS IIR, applications

13. Frequency domain adaptive filters, echo compensation

14. Adaptive filters with fast convergence

Syllabus of tutorials:

1. Introduction to MATLAB, basic sequences generation

2. Short-time spectral analysis, spectrograms

3. Parametric methods, basic functions

4. Signal analysis using parametric methods

5. Synthesis of AR nonstationary processes and their analysis

6. Implementation of LMS estimator

7. Implementation of LMS predictor, noise suppression

8. Application of LMS algorithm for equalization

9. Comparison of LMS, SLMS, NLMS and RLS algorithms

10. Convergence behaviour of adaptive filters

11. Frequency analysis with application to speech formants

12. Implementation of noise cancellation methods

13. Implementation of frequency domain LMS adaptive filter

14. Projects presentation, credit

Study Objective:

Study materials:

1. Haykin, S.: Adaptive Filter Theory. Prentice-Hall, Inc., New Jersey, 1991

2. Widrow, B., Stearns, S.D.: Adaptive Signal Processing. Prentice-Hall, Inc., New Jersey, 1985

3. Young, P.: Recursive Estimation and Time-Serie Analysis, Springer-Verlag, New York, 1984

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

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