Adaptive Filters

Contents

Part I – Basics

  • 1. Introduction and application examples (part 1)
  • 2. Signal models
  • 3. Error criteria and cost functions

Part II – Algorithms

  • 4. The Wiener filter
    • 4.1 Fundamentals
    • 4.2 Othogonality theorem
    • 4.3 Extensions of the Wiener filter
    • 4.4 Application examples
  • 5. Linear prediction
    • 5.1 Fundamentals
    • 5.2 Normal equation
    • 5.3 Rekursive calculation of the predictor coefficients
    • 5.4 Application Examples
  • 6. Algorithms for adaptive filters
    • 6.1 The “Normalized Least Mean Square” (NLMS) algorithm
    • 6.2 The filtered-x LMS algorithm
    • 6.3 Methods of affine projection
    • 6.4 The “Recursive Least Squares” (RLS) algorithm
    • 6.5 Processing structures
    • 6.6 Kalman filter
    • 6.7 Properties and comparison of the algorithms

Part III – Acoustic echo and noise control

  • 7. Fundamentals
  • 8. Echo cancellation
  • 9. Residual echo and noise suppression
  • 10. Beamforming
  • 11. Control of the algorithms and implementation issues

Part IV – Application examples

  • 12. Application examples (part 2)