E9 211 Adaptive Signal Processing

About E9 211 Adaptive Signal Processing

The primary aim of E9 211 is to develop a mathematical theory of linear adaptive filters. Adaptation is accomplished by adjusting the free parameters of a filter according to the input data to achieve the desired output. Such adaptive algorithms are frequently encountered in many signal processing and machine learning algorithms. The adaptive signal processing course provides a comprehensive treatment of mathematical signal processing algorithms for designing optimum and linear filters; designing, implementing, and analyzing adaptive filters applied to system identification, inverse modeling (deconvolution), adaptive control, and interference cancellation; and some selected emerging topics in signal processing.


Undergraduate courses on probability theory, digital signal processing, and linear algebra. First few lectures provide the required background in linear algebra and random processes.


Tuesdays and Thursdays 11:30am-1:00pm, Online via MS Teams.


Review of linear algebra and random processes. Optimal estimation. Linear estimation. Steepest-descent algorithms. Stochastic-gradient algorithms. Least squares and recursive least squares. Kalman filtering. Particle filtering. Blind deconvolution and beamforming. Subspace tracking. Robust adaptive filters. Iterative solvers of large-scale linear systems. Selected emerging topics.


  • T1. Adaptive Filters, by Ali H. Sayed, John Wiley & Sons, NJ, 2008

  • T1_1. Fundamentals of Adaptive Filtering, by Ali H. Sayed, Wiley student edition,2016

  • T2. Adaptive Signal Processing, by Bernard Widrow, Pearson, 2002

  • Other useful resources

    • Adaptive Signal Processing: Next-Generation Solutions, Tulay Adali and Simon Haykin, Wiley-India edition, 2010.

    • Adaptive Filter Theory, Simon Haykin, fourth edition, Pearson India, 2002.

Course requirements and grading

  • Three homeworks (programming): 10% each, i.e., 30% in total

    • Mandatory to participate in the final exam and to pass the course.

    • Prepare reports using LaTeX.

    • Submit only pdf files. Include Matlab scripts as appendices. Word documents will not be graded.

    • Late submissions are allowed, but will not be graded.

  • Midterm assessment on December 3, 2020: 20%

    • Open book written exam. 24 hour turn in time.

    • Syllabus covered till 19-11-2020, i.e., till and including Steepest-descent method.

  • Project: 30%

    • Prepare reports using LaTeX.

    • Submit only pdf files. Include Matlab scripts as appendices. Word documents will not be graded.

  • Final assessment on January 21, 2021: 20%

    • Open book written exam. 24 hour turn in time.

    • Needless to say, includes the entire syllabus.


Lecture numberDate Topic Reference Slides
1 08-10-2020 Introduction slides
2 13-10-2020 Linear algebra slides
315-10-2020 Linear algebra - contd. slides
420-10-2020 Optimization theory and random processes slides
522-10-2020 Exercise session
627-10-2020 Optimal estimation T1 - Ch. 1, Ch. 2.1-2.2 slides
729-10-2020 Linear estimation T1 - Ch. 3 and Ch. 4 slides
803-11-2020 Linear models T1 - Ch. 5 slides
905-11-2020 Constrained estimation and beamforming T1. Ch.6.1-6.4 and 6.5 slides
1010-11-2020 Exercise session T1: Examples 4.1,4.2, 5.5; Problems I.14, II.7, II.17, II.29
1112-11-2020 Direction estimation and beamforming
1217-11-2020 Steepest gradient descent T1. Ch 8 (T1_1. Ch.4) slides
1319-11-2020 Steepest gradient descent contd. T1. Ch 9 (except 9.8) (T1_1. Ch.4) slides
1424-11-2020 Newton's method and LMS T1. Ch. 9.8, 10 (except 10.7); T1_1 Ch. 5.1-5.4 slides
1526-11-2020 LMS contd. T1. Ch. 11 and Ch. 13 (except 10.7);Ch 5.6 and Ch. 5.8 slides
1601-12-2020 Exercise session T1: Problems III.1, III.8
1703-12-2020 Mid-term exam
1808-12-2020 Mid-term solutions, Leaky and Sign LMS T1. Ch 12.1 ; T1_1. Ch. 5.7.1
1910-12-2020 Constant modulus algorithm T1. Ch. 12.2; T1_1. Ch. 5.7.2
2015-12-2020 Recursive least squares T1. Ch 14 ; T1_1. Ch. 12
2117-12-2020 Kalman filter and weighted least squares T1. Ch 7; T1_1. Ch. 2.C slides
2222-12-2020 Kalman filter and recursive least squares T1. Ch 31; T1_1. Ch. 12.A
2324-12-2020 Adaptive linearly constrained beamforming paper
2429-12-2020 No class
2531-12-2020 No class
2605-01-2021 Total least squares and total least mean squares T1_1. Ch. 17.B
2707-01-2021 Course project discussion
2812-01-2021 Covariance estimation special topic
2914-01-2021 Graph signal processing special topic


This project consists of two parts on implementing and studying adaptive filters for adaptive noise cancellation: (a) using single-channel microphone recordings and (b) using multi-channel microphone recordings.

Description and Data

Demo by Prasobh Sankar


Previous exams