Adaptive Signal Processing With Applications

Catalog description

This course covers theory of adaptation with stationary signals; performance measures; least-mean squares (LMS), recursive least squares (RLS) algorithms; adaptation of generalized feed forward filters including polynomial filters, neural network-based filters, and functional expansion filters. The course also addresses the applications and implementations including speech processing, system identification, noise reduction, echo cancellation in the communication systems, active noise control, deconvolution and equalization, and blind source separation. Permission of department required. Typically offered Fall. Course Learning Outcomes 1. Understand concept and knowledge of adaptive filtering and applications. 2. Know least mean squares (LMS) algorithms. 3. Know recursive least mean squares (RLS) algorithms. 4. Understand advanced adaptive filters such as polynomial filters, neural network-based filters, and functional expansion filters. 5. Know adaptive filter applications such as system identification and noise cancellation problems. 6. Know adaptive filter applications such as speech processing, echo cancellation in communication system, active noise control, equalization and blind source separation. View Class Schedule