Later in the course we shall review various methods of designing frequency-selective filters satisfying prescribed specifications · We now describe several loworder- FIR and IIR digital filters with reasonable selective frequency responses that often are satisfactory in a number of applications Copyright 2001, S. K. Mitra

Objective-Determination- of realizable transfer function G() approximating a given frequency response specification is an important step in the development of a digital filter If an IIR filter is desired, G() should be a stable real rational function Digital filter design is the process of deriving the transfer function G

An LtI discrete-time- system is completely characterized in the time-domain by its impulse response {h[n]} We consider now the use of the DTFT and the z-transform in developing the transform- domain representations of an Lti system Copyright 2001, S. K. Mitra

8.1 The Modeling Process: Theory and Practice 8.2 Estimation of All-Pole Models 8.3 Estimation Of Pole-Zero Models 8.4 Applications 8.4.1 Spectral Estimation 8.4.2 Speech Modeling 8.5 Harmonic Models and Frequency Estimation Techniques 8.6 Summary

9.1 Typical Applications of Adaptive Filters 9.2 Principles of Adaptive Filters 9.3 Method of Steepest Descent 9.4 Least-Mean-Square Adaptive Filters 9.5 Recursive Least-Squares Adaptive Filters 9.6 Fast RLS Algorithms for FIR Filtering 9.7 Tracking Performance of Adaptive Algorithms 9.8 Summary

Z-Transform The DTFT provides a frequency-domain representation of discrete-time signals and LTI discrete-time systems Because of the convergence condition, in many cases. the DTFT of a sequence may not exist As a result, it is not possible to make use of such frequency-domain characterization in these cases

Comb Filters The simple filters discussed so far are characterized either by single passband and/or a single stopband There are applications where filters with multiple passbands and stopbands are required The comb filter is an example of such filters Copyright 2001, S. K. Mitra

1.1 Introduction Any problems about signal analyses and processing may be thought of letting signals trough systems. f(t) y(t) h(t) From f(t) and h(t), find y(t), Signal processing From f(t) and y(t), find h(t), System design From(t)andh(t), find(t), Signal reconstruction

2.1 Discrete-Time Signals: Time-Domain Representation Signals represented as sequences of numbers, called samples Sample value of a typical signal or sequence denoted as x[n] with n being an integer in the range-oo≤n≤∞ x[n] defined only for integer values of n and undefined for noninteger values of n Discrete-time signal represented by {x[n]}

4.1 LTI Discrete-Time Systems in the Transform Domain · Such transformdomain- representations provide additional insight into the behavior of such systems It is easier to design and implement these systems in the transform-domain for certain applications We consider now the use of the DtFt and the z-transform in developing the transform- domain representations of an LTI system