Contents

Announcement SS 2013

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)