Quadrature-Mirror Filter Bank

Size: px
Start display at page:

Download "Quadrature-Mirror Filter Bank"

Transcription

1 Quadrature-Mirror Filter Bank In many applications, a discrete-time signal x[n] is split into a number of subband signals { v k [ n]} by means of an analysis filter bank The subband signals are then processed Finally, the processed subband signals are combined by a synthesis filter bank resulting in an output signal y[n]

2 Quadrature-Mirror Filter Bank If the subband signals { v k [ n]} are bandlimited to frequency ranges much smaller than that of the original input signal x[n], they can bedown-sampled before processing Because of the lower sampling rate, the processing of the down-sampled signals can be carried out more efficiently

3 Quadrature-Mirror Filter Bank After processing, these signals are then upsampled before being combined by the synthesis filter bank into a higher-rate signal The combined structure is called a quadrature-mirror filter (QMF) bank 3

4 Quadrature-Mirror Filter Bank If the down-sampling and up-sampling factors are equal to or greater than the number of bands of the filter bank, then the output y[n] can be made to retain some or all of the characteristics of the input signal x[n] by choosing appropriately the filters in the structure 4

5 Quadrature-Mirror Filter Bank If the up-sampling and down-sampling factors are equal to the number of bands, then the structure is called a critically sampled filter bank The most common application of this scheme is in the efficient coding of a signal x[n] 5

6 Two-Channel QMF Bank Figure below shows the basic two-channel QMF bank-based subband codec (coder/decoder) 6

7 Two-Channel QMF Bank The analysis filters H 0 (z) and H (z) have typically a lowpass and highpass frequency responses, respectively, with a cutoff at π/ 7

8 Two-Channel QMF Bank Each down-sampled subband signal is encoded by exploiting the special spectral properties of the signal, such as energy levels and perceptual importance It follows from the figure that the sampling rates of the output y[n] and the input x[n] are the same 8

9 Two-Channel QMF Bank 9 The analysis and the synthesis filters are chosen so as to ensure that the reconstructed output y[n] is a reasonably close replica of the input x[n] Moreover, they are also designed to provide good frequency selectivity in order to ensure that the sum of the power of the subband signals is reasonably close to the input signal power

10 Two-Channel QMF Bank 0 In practice, various errors are generated in this scheme In addition to the coding error and errors caused by transmission of the coded signals through the channel, the QMF bank itself introduces several errors due to the sampling rate alterations and imperfect filters We ignore the coding and the channel errors

11 Two-Channel QMF Bank We investigate only the errors caused by the sampling rate alterations and their effects on the performance of the system To this end, we consider the QMF bank structure without the coders and the decoders as shown below

12 Analysis of the Two-Channel QMF Bank Making use of the input-output relations of the down-sampler and the up-sampler in the z-domain we arrive at V ( z) H ( z) X ( z), U k = k / / k ( z) = { V ( z ) + V ( z )}, k = 0, k ^ k ( z) = V U k k ( z )

13 Analysis of the Two-Channel QMF Bank From the first and the last equations we obtain after some algebra ^ k V ( z) { V ( z) + V ( z)} = = k { H ( z) X ( z) + H ( z) X ( z)} k k k 3 The reconstructed output of the filter bank is given by ^ ^ ( z) = G ( z) V ( z) G ( z) V ( z) Y 0 0 +

14 Analysis of the Two-Channel QMF Bank From the two equations of the previous slide we arrive at 4 Y H0( z) G0( z) + H( z) G ( z) = { H ( z) G ( z) + H ( z) G ( z)} X ( z) { ( z)} X ( z) The second term in the above equation is due to the aliasing caused by sampling rate alteration

15 Analysis of the Two-Channel QMF Bank 5 The input-output equation of the filter bank can be compactly written as Y( z) = T( z) X ( z) + A( z) X ( z) where T(z), called the distortion transfer function, is given by T 0 0 ( z) = { H ( z) G ( z) + H ( z) G ( z)} and ( z) = { H ( z) G ( z) + H ( z) G ( z)} A 0 0

16 Alias-Free Filter Bank 6 Since the up-sampler and the down-sampler are linear time-varying components, in general, the -channel QMF structure is a linear time-varying system It can be shown that the -channel QMF structure has a period of However, it is possible to choose the analysis and synthesis filters such that the aliasing effect is canceled resulting in a time-invariant operation

17 Alias-Free Filter Bank 7 To cancel aliasing we need to ensure that A(z) = 0, i.e., H ( z) G ( z) + H ( z) G ( z) = For aliasing cancellation we can choose G ( z) H ( z) 0 = G ( z) H0( z) This yields G ( z) C( z) H ( z), G ( z) = C( z) H ( z), 0 = 0 where C(z) is an arbitrary rational function

18 Alias-Free Filter Bank 8 If the above relations hold, then the QMF system is time-invariant with an inputoutput relation given by Y(z) = T(z)X(z) where T( z) = { H ( z) H ( z) + H ( z) H ( z)} 0 0 On the unit circle, we have jω jω jω jω jφ ω) Y ( e ) = T( e ) X ( e ) = T( e ) e X ( e ( jω )

19 9 Alias-Free Filter Bank If T(z) is an allpass function, i.e., T( e j ) = with d 0 then jω jω Y ( e ) = d X ( e ) indicating that the output of the QMF bank has the same magnitude response as that of the input (scaled by d) but exhibits phase distortion The filter bank is said to be magnitude preserving ω d

20 Alias-Free Filter Bank If T(z) has linear phase, i.e., jω arg{ T( e )} = φ( ω) = αω + β then jω jω arg{ Y( e )} = arg{ X ( e )}+ αω + β The filter bank is said to be phasepreserving but exhibits magnitude distortion 0

21 Alias-Free Filter Bank If an alias-free filter bank has no magnitude and phase distortion, then it is called a perfect reconstruction (PR) QMF bank l In such a case, T ( z) = dz resulting in l Y( z) = dz X ( z) In the time-domain, the input-output relation for all possible inputs is given by y[ n] = d x[ n l]

22 Alias-Free Filter Bank Thus, for a perfect reconstruction QMF bank, the output is a scaled, delayed replica of the input Example - Consider the system shown below x[n] z z + y[n]

23 Alias-Free Filter Bank 3 Comparing this structure with the general QMF bank structure we conclude that here we have H ( z) =, H ( z) = z, G ( z) = z, G ( z) 0 0 = Substituting these values in the expressions for T(z) and A(z) we get ( z + z = ( z ) = 0 T ( z) = ) z A( z) = z

24 Alias-Free Filter Bank Thus the simple multirate structure is an alias-free perfect reconstruction filter bank However, the filters in the bank do not provide any frequency selectivity 4

25 An Alias-Free Realization 5 A very simple alias-free -channel QMF bank is obtained when H ( z) = H 0 ( z) The above condition, in the case of a real coefficient filter, implies jω j( π ω) H( e ) = H0( e ) indicating that if H 0 (z) is a lowpass filter, then H (z) is a highpass filter, and vice versa

26 An Alias-Free Realization jω j( π ω) The relation H( e ) = H0( e ) jω indicates that H ( e ) is a mirror-image jω of H 0 ( e ) with respect to π/, the quadrature frequency This has given rise to the name quadraturemirror filter bank 6

27 An Alias-Free Realization 7 Substituting H ( z) = H 0 ( z) in G0( z) = C( z) H( z), G( z) = C( z) H0( z), with C(z) = we get G0( z) = H( z), G( z) = H( z) = H0( z) The above equations imply that the two analysis filters and the two synthesis filters are essentially determined from one transfer function H 0 (z)

28 An Alias-Free Realization Moreover, if H 0 (z) is a lowpass filter, then G 0 (z) is also a lowpass filter and G (z) is a highpass filter The distortion function in this case reduces to T( z) = { H ( z) H ( z)} = { H ( z) H ( z)}

29 An Alias-Free Realization A computationally efficient realization of the above QMF bank is obtained by realizing the analysis and synthesis filters in polyphase form Let the -band Type polyphase representation of H 0 (z) be given by H ( z) = E ( z ) + z E ( ) 0 0 z 9

30 An Alias-Free Realization Then from the relation H ( z) = H 0 ( z) it follows that H( z) = E0( z ) z E( z ) Combining the last two equations in a matrix form we get H H 0 0 ( z) ( z) = 0( ) E z z E ( z ) 30

31 An Alias-Free Realization Likewise, the synthesis filters can be expressed in a matrix form as [ ] = [ G ] Making use of the last two equations we can 0( z) G ( z) z E( z ) E0( z ) redraw the two-channel QMF bank as shown below 3

32 An Alias-Free Realization Making use of the cascade equivalences, the above structure can be further simplified as shown below 3

33 An Alias-Free Realization Substituting the polyphase representations of the analysis filters we arrive at the expression for the distortion function T(z) in terms of the polyphase components as T( z) = z E ( z ) E ( z 0 ) 33

34 An Alias-Free Realization 34 Example - Let Its polyphase components are Hence H ) = + z 0( z H( z) = H0( z) = E0( z ) z E( z ) = z Likewise G ( z) = z E ( z ) + E ( z ) = + z E0 ( z ) =, E( z ) = 0 0 ( z) z E( z ) E0( z = G = ) + z

35 An Alias-Free Realization The distortion transfer function for this realization is thus given by T ( z) = z E ( z ) E ( z ) = z 0 The resulting structure is a perfect reconstruction QMF bank 35

36 Alias-Free FIR QMF Bank 36 If in the above alias-free QMF bank H 0( z ) is a linear-phase FIR filter, then its polyphase components E 0( z ) and E ( z), are also linear-phase FIR transfer functions In this case, T ( z) = z E ( ) ( 0 z E z ) exhibits a linear-phase characteristic As a result, the corresponding -channel QMF bank has no phase distortion

37 Alias-Free FIR QMF Bank 37 However, in general T ( ) is not a constant, and as a result, the QMF bank exhibits magnitude distortion We next outline a method to minimize the residual amplitude distortion Let H 0( z ) be a length-n real-coefficient linear-phase FIR transfer function: H e j ω N = 0 ( z) n= 0h0[ n] z n

38 Alias-Free FIR QMF Bank 38 Note: H 0( z ) can either be a Type or a Type linear-phase FIR transfer function since it has to be a lowpass filter Then h 0[ n ] satisfy the condition h0[ n] = h0[ N n] In this case we can write H ( ) / 0 e j ω = e j ω N ~ H0( ω) In the above H~ 0( ω ) is the amplitude function, a real function of ω

39 Alias-Free FIR QMF Bank 39 The frequency response of the distortion transfer function can now be written as jnω T ( e jω) = e { H ( ω) ( ) ( ( π ω) ) 0 e j N H j 0 e } From the above, it can be seen that if N is even, then T ( e jω ) = 0 at ω = π/, implying severe amplitude distortion at the output of the filter bank

40 Alias-Free FIR QMF Bank 40 N must be odd, in which case we have jnω T e jω ) = e { H ( ) ( ( ) 0 e jω + H0 e j π ω ) ω = e jn { H ( ) ( ) 0 e jω + H e jω } It follows from the above that the FIR - channel QMF bank will be of perfect reconstruction type if H ( e jω) + H ( e jω) ( 0 = }

41 Alias-Free FIR QMF Bank 4 Now, the -channel QMF bank with linearphase filters has no phase distortion, but will always exhibit amplitude distortion jω unless T( e ) is a constant for all ω If H 0 ( z) is a very good lowpass filter with H0( e jω ) in the passband and H0( e jω ) 0 in the stopband, then H ( z ) is a very good highpass filter with its passband coinciding with the stopband of H 0( z ), and vice-versa

42 Alias-Free FIR QMF Bank jω As a result, T( e ) / in the passbands of H 0( z ) and H ( z) Amplitude distortion occurs primarily in the transition band of these filters Degree of distortion determined by the amount of overlap between their squaredmagnitude responses 4

43 Alias-Free FIR QMF Bank 43 This distortion can be minimized by controlling the overlap, which in turn can be controlled by appropriately choosing the passband edge of H 0( z ) One way to minimize the amplitude distortion is to iteratively adjust the filter coefficients h 0 [n] of H 0 ( z) on a computer such that jω jω H0( e ) + H( e ) is satisfied for all values of ω

44 Alias-Free FIR QMF Bank To this end, the objective function φ to be minimized can be chosen as a linear combination of two functions: () stopband attenuation of H 0 ( z), and () sum of squared magnitude responses of H ( ) and H (z) 0 z 44

45 Alias-Free FIR QMF Bank 45 One such objective function is given by φ = αφ+ ( α) φ where π φ H e jω = ( ) d ω ω and s π jω jω φ = H0( e ) H( e ) dω 0 and 0 < α <, and ωs = π + ε for some small ε > 0

46 Alias-Free FIR QMF Bank 46 Since T ( ) is symmetric with respect to π/, the second integral in the objective function φ can be replaced with φ e j ω π/ ω ω = H0( e j ) H( e j ) 0 dω After φ has been made very small by the optimization procedure, both φ and φ will also be very small

47 Alias-Free FIR QMF Bank Using this approach, Johnston has designed a large class of linear-phase FIR filters meeting a variety of specifications and has tabulated their impulse response coefficients Program 0_9 can be used to verify the performance of Johnston s filters 47

48 Alias-Free FIR QMF Bank Example- The gain responses of the length- linear-phase FIR lowpass filter B and its power-complementary highpass filter obtained using Program 0_9 are shown below 0 0 H 0 (z) H (z) Gain, db ω/π

49 Alias-Free FIR QMF Bank The program then computes the amplitude distortion H 0 ( e j ω ) + H( e j ω) in db as shown below Amplitude distortion in db ω/π

50 Alias-Free FIR QMF Bank From the gain response plot it can be seen that the stopband edge ω s of the lowpass filterb is about 0.7π, which corresponds to a transition bandwidth of ω 0.5π) / = 0. 05π ( s The minimum stopband attenuation is approximately 34 db 50

51 Alias-Free FIR QMF Bank The amplitude distortion function is very close to 0 db in both the passbands and the stopbands of the two filters, with a peak value of ± 0.0 db 5

52 Alias-Free IIR QMF Bank 5 Under the alias-free conditions of G0( z) = H( z), G ( z) = H0( z) and the relation H( z) = H0( z), the distortion function T(z) is given by T ( z) = z E ( ) ( 0 z E z ) If T(z) is an allpass function, then its magnitude response is a constant, and as a result its corresponding QMF bank has no magnitude distortion

53 Alias-Free IIR QMF Bank 53 Let the polyphase components E 0 ( z) and E ( z ) of H 0( z ) be expressed as E z) = A ( ), E z) = A ( ) 0( 0 z ( z with A 0( z) and A ( z) being stable allpass functions Thus, H ( z) = H [ A ( z) + z A ( )] 0 0 z ( ) ( ) ( z = A 0 z z A z [ )]

54 Alias-Free IIR QMF Bank 54 In matrix form, the analysis filters can be expressed as H ( ) ( 0 z = A0 z ) H ( ) ( 0 z z A z ) The corresponding synthesis filters in matrix form are given by G ( ) ( ) = ( ) ( ) 0 z G z z A z A0 z [ ] [ ]

55 Alias-Free IIR QMF Bank Thus, the synthesis filters are given by ( ) ( ) ( z = [ A0 z + z A z )] = H( z G 0( z) = [ A0( z ) + z A( z )] = H0( z) G The realization of the magnitude-preserving -channel QMF bank is shown below ) 55

56 Alias-Free IIR QMF Bank From H ( ) [ ( ) ( ] 0 z = A0 z + z A z ) it can be seen that the lowpass transfer function H 0 (z) has a polyphase-like decomposition, except here the polyphase components are stable allpass transfer functions 56

57 Alias-Free IIR QMF Bank 57 It has been shown earlier that a boundedreal (BR) transfer function H0 ( z) = P0 ( z)/ D( z) of odd order, with no common factors between its numerator and denominator, can be expressed in the form H ( ) [ ( ) ( ] 0 z = A0 z + z A z ) if it satisfies the power-symmetry condition H0 ( z) H0( z ) + H0( z) H0( z ) = and has a symmetric numerator P 0 (z)

58 Alias-Free IIR QMF Bank 58 It has also been shown that any odd-order elliptic lowpass half-band filter H 0 (z) with a frequency response specification given by jω δ p H( e ), for 0 ω ω p j H( e ω ) δ s, for ωs ω π and satisfying the conditions ω p + ω s = π and δ s = 4δ p ( δ p ) can always be expressed in the form H [ A ( z) + z A ( )] 0 0 z ( z) =

59 Alias-Free IIR QMF Bank The poles of the elliptic filter satisfying the two conditions on bandedges and ripples lie on the imaginary axis Using the pole-interlacing property discussed earlier, on can readily identify the expressions for the two allpass transfer functions A 0 (z) and A (z) 59

60 Alias-Free IIR QMF Bank Example - The frequency response specifications of a real-coefficient lowpass half-band filter are given by: ω p= 0. 4π, ωs= 0. 6π, and δ s = From δ s = 4δ p ( δ p ) we get δ p = In db, the passband and stopband ripples are Rp = and Rs =

61 Alias-Free IIR QMF Bank Using the M-file ellipord we determine the minimum order of the elliptic lowpass filter to be 5 Next, using the M-file ellip the transfer function of the lowpass filter is determined whose gain response is shown below 0 Real half-band filter Gain, db ω/π

62 Alias-Free IIR QMF Bank The poles obtained using the function tfzp are at z = 0, z = ± j , and z = ± j The pole-zero plot obtained using zplane is shown below Imaginary Part Real Part

63 Alias-Free IIR QMF Bank Using the pole-interlacing property we arrive at the transfer functions of the two allpass filters as given below: z A 0( z ) = z A ( z z ) = z 63

64 64

The basic structure of the L-channel QMF bank is shown below

The basic structure of the L-channel QMF bank is shown below -Channel QMF Bans The basic structure of the -channel QMF ban is shown below The expressions for the -transforms of various intermediate signals in the above structure are given by Copyright, S. K. Mitra

More information

Perfect Reconstruction Two- Channel FIR Filter Banks

Perfect Reconstruction Two- Channel FIR Filter Banks Perfect Reconstruction Two- Channel FIR Filter Banks A perfect reconstruction two-channel FIR filter bank with linear-phase FIR filters can be designed if the power-complementary requirement e jω + e jω

More information

Multirate signal processing

Multirate signal processing Multirate signal processing Discrete-time systems with different sampling rates at various parts of the system are called multirate systems. The need for such systems arises in many applications, including

More information

Multirate Digital Signal Processing

Multirate Digital Signal Processing Multirate Digital Signal Processing Basic Sampling Rate Alteration Devices Up-sampler - Used to increase the sampling rate by an integer factor Down-sampler - Used to decrease the sampling rate by an integer

More information

ECE503: Digital Signal Processing Lecture 5

ECE503: Digital Signal Processing Lecture 5 ECE53: Digital Signal Processing Lecture 5 D. Richard Brown III WPI 3-February-22 WPI D. Richard Brown III 3-February-22 / 32 Lecture 5 Topics. Magnitude and phase characterization of transfer functions

More information

Digital Signal Processing Lecture 8 - Filter Design - IIR

Digital Signal Processing Lecture 8 - Filter Design - IIR Digital Signal Processing - Filter Design - IIR Electrical Engineering and Computer Science University of Tennessee, Knoxville October 20, 2015 Overview 1 2 3 4 5 6 Roadmap Discrete-time signals and systems

More information

Basic Multi-rate Operations: Decimation and Interpolation

Basic Multi-rate Operations: Decimation and Interpolation 1 Basic Multirate Operations 2 Interconnection of Building Blocks 1.1 Decimation and Interpolation 1.2 Digital Filter Banks Basic Multi-rate Operations: Decimation and Interpolation Building blocks for

More information

Digital Signal Processing

Digital Signal Processing COMP ENG 4TL4: Digital Signal Processing Notes for Lecture #20 Wednesday, October 22, 2003 6.4 The Phase Response and Distortionless Transmission In most filter applications, the magnitude response H(e

More information

Stability Condition in Terms of the Pole Locations

Stability Condition in Terms of the Pole Locations Stability Condition in Terms of the Pole Locations A causal LTI digital filter is BIBO stable if and only if its impulse response h[n] is absolutely summable, i.e., 1 = S h [ n] < n= We now develop a stability

More information

Chapter 7: IIR Filter Design Techniques

Chapter 7: IIR Filter Design Techniques IUST-EE Chapter 7: IIR Filter Design Techniques Contents Performance Specifications Pole-Zero Placement Method Impulse Invariant Method Bilinear Transformation Classical Analog Filters DSP-Shokouhi Advantages

More information

Digital Speech Processing Lecture 10. Short-Time Fourier Analysis Methods - Filter Bank Design

Digital Speech Processing Lecture 10. Short-Time Fourier Analysis Methods - Filter Bank Design Digital Speech Processing Lecture Short-Time Fourier Analysis Methods - Filter Bank Design Review of STFT j j ˆ m ˆ. X e x[ mw ] [ nˆ m] e nˆ function of nˆ looks like a time sequence function of ˆ looks

More information

Course and Wavelets and Filter Banks. Filter Banks (contd.): perfect reconstruction; halfband filters and possible factorizations.

Course and Wavelets and Filter Banks. Filter Banks (contd.): perfect reconstruction; halfband filters and possible factorizations. Course 18.327 and 1.130 Wavelets and Filter Banks Filter Banks (contd.): perfect reconstruction; halfband filters and possible factorizations. Product Filter Example: Product filter of degree 6 P 0 (z)

More information

Digital Signal Processing

Digital Signal Processing COMP ENG 4TL4: Digital Signal Processing Notes for Lecture #21 Friday, October 24, 2003 Types of causal FIR (generalized) linear-phase filters: Type I: Symmetric impulse response: with order M an even

More information

Filter Banks II. Prof. Dr.-Ing. G. Schuller. Fraunhofer IDMT & Ilmenau University of Technology Ilmenau, Germany

Filter Banks II. Prof. Dr.-Ing. G. Schuller. Fraunhofer IDMT & Ilmenau University of Technology Ilmenau, Germany Filter Banks II Prof. Dr.-Ing. G. Schuller Fraunhofer IDMT & Ilmenau University of Technology Ilmenau, Germany Page Modulated Filter Banks Extending the DCT The DCT IV transform can be seen as modulated

More information

Module 4 MULTI- RESOLUTION ANALYSIS. Version 2 ECE IIT, Kharagpur

Module 4 MULTI- RESOLUTION ANALYSIS. Version 2 ECE IIT, Kharagpur Module MULTI- RESOLUTION ANALYSIS Version ECE IIT, Kharagpur Lesson Multi-resolution Analysis: Theory of Subband Coding Version ECE IIT, Kharagpur Instructional Objectives At the end of this lesson, the

More information

Filter structures ELEC-E5410

Filter structures ELEC-E5410 Filter structures ELEC-E5410 Contents FIR filter basics Ideal impulse responses Polyphase decomposition Fractional delay by polyphase structure Nyquist filters Half-band filters Gibbs phenomenon Discrete-time

More information

Multi-rate Signal Processing 3. The Polyphase Representation

Multi-rate Signal Processing 3. The Polyphase Representation Multi-rate Signal Processing 3. The Polyphase Representation Electrical & Computer Engineering University of Maryland, College Park Acknowledgment: ENEE630 slides were based on class notes developed by

More information

DIGITAL SIGNAL PROCESSING UNIT III INFINITE IMPULSE RESPONSE DIGITAL FILTERS. 3.6 Design of Digital Filter using Digital to Digital

DIGITAL SIGNAL PROCESSING UNIT III INFINITE IMPULSE RESPONSE DIGITAL FILTERS. 3.6 Design of Digital Filter using Digital to Digital DIGITAL SIGNAL PROCESSING UNIT III INFINITE IMPULSE RESPONSE DIGITAL FILTERS Contents: 3.1 Introduction IIR Filters 3.2 Transformation Function Derivation 3.3 Review of Analog IIR Filters 3.3.1 Butterworth

More information

ECSE 512 Digital Signal Processing I Fall 2010 FINAL EXAMINATION

ECSE 512 Digital Signal Processing I Fall 2010 FINAL EXAMINATION FINAL EXAMINATION 9:00 am 12:00 pm, December 20, 2010 Duration: 180 minutes Examiner: Prof. M. Vu Assoc. Examiner: Prof. B. Champagne There are 6 questions for a total of 120 points. This is a closed book

More information

Optimum Ordering and Pole-Zero Pairing of the Cascade Form IIR. Digital Filter

Optimum Ordering and Pole-Zero Pairing of the Cascade Form IIR. Digital Filter Optimum Ordering and Pole-Zero Pairing of the Cascade Form IIR Digital Filter There are many possible cascade realiations of a higher order IIR transfer function obtained by different pole-ero pairings

More information

Analysis of Finite Wordlength Effects

Analysis of Finite Wordlength Effects Analysis of Finite Wordlength Effects Ideally, the system parameters along with the signal variables have infinite precision taing any value between and In practice, they can tae only discrete values within

More information

Computer-Aided Design of Digital Filters. Digital Filters. Digital Filters. Digital Filters. Design of Equiripple Linear-Phase FIR Filters

Computer-Aided Design of Digital Filters. Digital Filters. Digital Filters. Digital Filters. Design of Equiripple Linear-Phase FIR Filters Computer-Aided Design of Digital Filters The FIR filter design techniques discussed so far can be easily implemented on a computer In addition, there are a number of FIR filter design algorithms that rely

More information

Multi-rate Signal Processing 7. M-channel Maximally Decmiated Filter Banks

Multi-rate Signal Processing 7. M-channel Maximally Decmiated Filter Banks Multi-rate Signal Processing 7. M-channel Maximally Decmiated Filter Banks Electrical & Computer Engineering University of Maryland, College Park Acknowledgment: ENEE630 slides were based on class notes

More information

Subband Coding and Wavelets. National Chiao Tung University Chun-Jen Tsai 12/04/2014

Subband Coding and Wavelets. National Chiao Tung University Chun-Jen Tsai 12/04/2014 Subband Coding and Wavelets National Chiao Tung Universit Chun-Jen Tsai /4/4 Concept of Subband Coding In transform coding, we use N (or N N) samples as the data transform unit Transform coefficients are

More information

Chapter 5 Frequency Domain Analysis of Systems

Chapter 5 Frequency Domain Analysis of Systems Chapter 5 Frequency Domain Analysis of Systems CT, LTI Systems Consider the following CT LTI system: xt () ht () yt () Assumption: the impulse response h(t) is absolutely integrable, i.e., ht ( ) dt< (this

More information

Filter Banks II. Prof. Dr.-Ing Gerald Schuller. Fraunhofer IDMT & Ilmenau Technical University Ilmenau, Germany

Filter Banks II. Prof. Dr.-Ing Gerald Schuller. Fraunhofer IDMT & Ilmenau Technical University Ilmenau, Germany Filter Banks II Prof. Dr.-Ing Gerald Schuller Fraunhofer IDMT & Ilmenau Technical University Ilmenau, Germany Prof. Dr.-Ing. G. Schuller, shl@idmt.fraunhofer.de Page Modulated Filter Banks Extending the

More information

Tunable IIR Digital Filters

Tunable IIR Digital Filters Tunable IIR Digital Filters We have described earlier two st-order and two nd-order IIR digital transfer functions with tunable frequency response characteristics We shall show now that these transfer

More information

Transform Analysis of Linear Time-Invariant Systems

Transform Analysis of Linear Time-Invariant Systems Transform Analysis of Linear Time-Invariant Systems Discrete-Time Signal Processing Chia-Ping Chen Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung, Taiwan ROC Transform

More information

Lecture 11: Two Channel Filter Bank

Lecture 11: Two Channel Filter Bank WAVELETS AND MULTIRATE DIGITAL SIGNAL PROCESSING Lecture 11: Two Channel Filter Bank Prof.V.M.Gadre, EE, IIT Bombay 1 Introduction In the previous lecture we studied Z domain analysis of two channel filter

More information

We have shown earlier that the 1st-order lowpass transfer function

We have shown earlier that the 1st-order lowpass transfer function Tunable IIR Digital Filters We have described earlier two st-order and two nd-order IIR digital transfer functions with tunable frequency response characteristics We shall show now that these transfer

More information

DIGITAL SIGNAL PROCESSING. Chapter 6 IIR Filter Design

DIGITAL SIGNAL PROCESSING. Chapter 6 IIR Filter Design DIGITAL SIGNAL PROCESSING Chapter 6 IIR Filter Design OER Digital Signal Processing by Dr. Norizam Sulaiman work is under licensed Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

More information

Digital Signal Processing IIR Filter Design via Bilinear Transform

Digital Signal Processing IIR Filter Design via Bilinear Transform Digital Signal Processing IIR Filter Design via Bilinear Transform D. Richard Brown III D. Richard Brown III 1 / 12 Basic Procedure We assume here that we ve already decided to use an IIR filter. The basic

More information

Digital Signal Processing

Digital Signal Processing COMP ENG 4TL4: Digital Signal Processing Notes for Lecture #24 Tuesday, November 4, 2003 6.8 IIR Filter Design Properties of IIR Filters: IIR filters may be unstable Causal IIR filters with rational system

More information

Filter Analysis and Design

Filter Analysis and Design Filter Analysis and Design Butterworth Filters Butterworth filters have a transfer function whose squared magnitude has the form H a ( jω ) 2 = 1 ( ) 2n. 1+ ω / ω c * M. J. Roberts - All Rights Reserved

More information

EE482: Digital Signal Processing Applications

EE482: Digital Signal Processing Applications Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu EE482: Digital Signal Processing Applications Spring 2014 TTh 14:30-15:45 CBC C222 Lecture 05 IIR Design 14/03/04 http://www.ee.unlv.edu/~b1morris/ee482/

More information

DESIGN OF ALIAS-FREE LINEAR PHASE QUADRATURE MIRROR FILTER BANKS USING EIGENVALUE-EIGENVECTOR APPROACH

DESIGN OF ALIAS-FREE LINEAR PHASE QUADRATURE MIRROR FILTER BANKS USING EIGENVALUE-EIGENVECTOR APPROACH DESIGN OF ALIAS-FREE LINEAR PHASE QUADRATURE MIRROR FILTER BANKS USING EIGENVALUE-EIGENVECTOR APPROACH ABSTRACT S. K. Agrawal 1# and O. P. Sahu 1& Electronics and Communication Engineering Department,

More information

Digital Filter Structures

Digital Filter Structures Chapter 8 Digital Filter Structures 清大電機系林嘉文 cwlin@ee.nthu.edu.tw 03-5731152 8-1 Block Diagram Representation The convolution sum description of an LTI discrete-time system can, in principle, be used to

More information

UNIVERSITY OF OSLO. Faculty of mathematics and natural sciences. Forslag til fasit, versjon-01: Problem 1 Signals and systems.

UNIVERSITY OF OSLO. Faculty of mathematics and natural sciences. Forslag til fasit, versjon-01: Problem 1 Signals and systems. UNIVERSITY OF OSLO Faculty of mathematics and natural sciences Examination in INF3470/4470 Digital signal processing Day of examination: December 1th, 016 Examination hours: 14:30 18.30 This problem set

More information

ENEE630 ADSP RECITATION 5 w/ solution Ver

ENEE630 ADSP RECITATION 5 w/ solution Ver ENEE630 ADSP RECITATION 5 w/ solution Ver20209 Consider the structures shown in Fig RI, with input transforms and filter responses as indicated Sketch the quantities Y 0 (e jω ) and Y (e jω ) Figure RI:

More information

EE 521: Instrumentation and Measurements

EE 521: Instrumentation and Measurements Aly El-Osery Electrical Engineering Department, New Mexico Tech Socorro, New Mexico, USA November 1, 2009 1 / 27 1 The z-transform 2 Linear Time-Invariant System 3 Filter Design IIR Filters FIR Filters

More information

MULTIRATE DIGITAL SIGNAL PROCESSING

MULTIRATE DIGITAL SIGNAL PROCESSING MULTIRATE DIGITAL SIGNAL PROCESSING Signal processing can be enhanced by changing sampling rate: Up-sampling before D/A conversion in order to relax requirements of analog antialiasing filter. Cf. audio

More information

MULTIRATE SYSTEMS. work load, lower filter order, lower coefficient sensitivity and noise,

MULTIRATE SYSTEMS. work load, lower filter order, lower coefficient sensitivity and noise, MULIRAE SYSEMS ransfer signals between two systems that operate with different sample frequencies Implemented system functions more efficiently by using several sample rates (for example narrow-band filters).

More information

Near-perfect-reconstruction low-complexity two-band IIR/FIR QMF banks with FIR phase-compensation filters

Near-perfect-reconstruction low-complexity two-band IIR/FIR QMF banks with FIR phase-compensation filters Signal Processing 86 (26) 171 181 www.elsevier.com/locate/sigpro Near-perfect-reconstruction low-complexity two-band IIR/FIR QMF banks with FIR phase-compensation filters Jo rg Kliewer a,, Enisa Brka b,1

More information

2. Typical Discrete-Time Systems All-Pass Systems (5.5) 2.2. Minimum-Phase Systems (5.6) 2.3. Generalized Linear-Phase Systems (5.

2. Typical Discrete-Time Systems All-Pass Systems (5.5) 2.2. Minimum-Phase Systems (5.6) 2.3. Generalized Linear-Phase Systems (5. . Typical Discrete-Time Systems.1. All-Pass Systems (5.5).. Minimum-Phase Systems (5.6).3. Generalized Linear-Phase Systems (5.7) .1. All-Pass Systems An all-pass system is defined as a system which has

More information

Optimum Ordering and Pole-Zero Pairing. Optimum Ordering and Pole-Zero Pairing Consider the scaled cascade structure shown below

Optimum Ordering and Pole-Zero Pairing. Optimum Ordering and Pole-Zero Pairing Consider the scaled cascade structure shown below Pole-Zero Pairing of the Cascade Form IIR Digital Filter There are many possible cascade realiations of a higher order IIR transfer function obtained by different pole-ero pairings and ordering Each one

More information

1 1.27z z 2. 1 z H 2

1 1.27z z 2. 1 z H 2 E481 Digital Signal Processing Exam Date: Thursday -1-1 16:15 18:45 Final Exam - Solutions Dan Ellis 1. (a) In this direct-form II second-order-section filter, the first stage has

More information

Design of IIR filters

Design of IIR filters Design of IIR filters Standard methods of design of digital infinite impulse response (IIR) filters usually consist of three steps, namely: 1 design of a continuous-time (CT) prototype low-pass filter;

More information

Chapter 5 Frequency Domain Analysis of Systems

Chapter 5 Frequency Domain Analysis of Systems Chapter 5 Frequency Domain Analysis of Systems CT, LTI Systems Consider the following CT LTI system: xt () ht () yt () Assumption: the impulse response h(t) is absolutely integrable, i.e., ht ( ) dt< (this

More information

Electronic Circuits EE359A

Electronic Circuits EE359A Electronic Circuits EE359A Bruce McNair B26 bmcnair@stevens.edu 21-216-5549 Lecture 22 578 Second order LCR resonator-poles V o I 1 1 = = Y 1 1 + sc + sl R s = C 2 s 1 s + + CR LC s = C 2 sω 2 s + + ω

More information

DISCRETE-TIME SIGNAL PROCESSING

DISCRETE-TIME SIGNAL PROCESSING THIRD EDITION DISCRETE-TIME SIGNAL PROCESSING ALAN V. OPPENHEIM MASSACHUSETTS INSTITUTE OF TECHNOLOGY RONALD W. SCHÄFER HEWLETT-PACKARD LABORATORIES Upper Saddle River Boston Columbus San Francisco New

More information

LINEAR-PHASE FIR FILTERS DESIGN

LINEAR-PHASE FIR FILTERS DESIGN LINEAR-PHASE FIR FILTERS DESIGN Prof. Siripong Potisuk inimum-phase Filters A digital filter is a minimum-phase filter if and only if all of its zeros lie inside or on the unit circle; otherwise, it is

More information

UNIVERSITY OF OSLO. Please make sure that your copy of the problem set is complete before you attempt to answer anything.

UNIVERSITY OF OSLO. Please make sure that your copy of the problem set is complete before you attempt to answer anything. UNIVERSITY OF OSLO Faculty of mathematics and natural sciences Examination in INF3470/4470 Digital signal processing Day of examination: December 9th, 011 Examination hours: 14.30 18.30 This problem set

More information

! Introduction. ! Discrete Time Signals & Systems. ! Z-Transform. ! Inverse Z-Transform. ! Sampling of Continuous Time Signals

! Introduction. ! Discrete Time Signals & Systems. ! Z-Transform. ! Inverse Z-Transform. ! Sampling of Continuous Time Signals ESE 531: Digital Signal Processing Lec 25: April 24, 2018 Review Course Content! Introduction! Discrete Time Signals & Systems! Discrete Time Fourier Transform! Z-Transform! Inverse Z-Transform! Sampling

More information

Design of Nonuniform Filter Banks with Frequency Domain Criteria

Design of Nonuniform Filter Banks with Frequency Domain Criteria Blekinge Institute of Technology Research Report No 24:3 Design of Nonuniform Filter Banks with Frequency Domain Criteria Jan Mark de Haan Sven Nordholm Ingvar Claesson School of Engineering Blekinge Institute

More information

ECE 410 DIGITAL SIGNAL PROCESSING D. Munson University of Illinois Chapter 12

ECE 410 DIGITAL SIGNAL PROCESSING D. Munson University of Illinois Chapter 12 . ECE 40 DIGITAL SIGNAL PROCESSING D. Munson University of Illinois Chapter IIR Filter Design ) Based on Analog Prototype a) Impulse invariant design b) Bilinear transformation ( ) ~ widely used ) Computer-Aided

More information

Optimal Design of Real and Complex Minimum Phase Digital FIR Filters

Optimal Design of Real and Complex Minimum Phase Digital FIR Filters Optimal Design of Real and Complex Minimum Phase Digital FIR Filters Niranjan Damera-Venkata and Brian L. Evans Embedded Signal Processing Laboratory Dept. of Electrical and Computer Engineering The University

More information

ECE 350 Signals and Systems Spring 2011 Final Exam - Solutions. Three 8 ½ x 11 sheets of notes, and a calculator are allowed during the exam.

ECE 350 Signals and Systems Spring 2011 Final Exam - Solutions. Three 8 ½ x 11 sheets of notes, and a calculator are allowed during the exam. ECE 35 Spring - Final Exam 9 May ECE 35 Signals and Systems Spring Final Exam - Solutions Three 8 ½ x sheets of notes, and a calculator are allowed during the exam Write all answers neatly and show your

More information

Digital Filter Structures. Basic IIR Digital Filter Structures. of an LTI digital filter is given by the convolution sum or, by the linear constant

Digital Filter Structures. Basic IIR Digital Filter Structures. of an LTI digital filter is given by the convolution sum or, by the linear constant Digital Filter Chapter 8 Digital Filter Block Diagram Representation Equivalent Basic FIR Digital Filter Basic IIR Digital Filter. Block Diagram Representation In the time domain, the input-output relations

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 3 Signals & Systems Prof. ark Fowler Note Set #28 D-T Systems: DT Filters Ideal & Practical /4 Ideal D-T Filters Just as in the CT case we can specify filters. We looked at the ideal filter for the

More information

Lecture 7 Discrete Systems

Lecture 7 Discrete Systems Lecture 7 Discrete Systems EE 52: Instrumentation and Measurements Lecture Notes Update on November, 29 Aly El-Osery, Electrical Engineering Dept., New Mexico Tech 7. Contents The z-transform 2 Linear

More information

EE 313 Linear Signals & Systems (Fall 2018) Solution Set for Homework #7 on Infinite Impulse Response (IIR) Filters CORRECTED

EE 313 Linear Signals & Systems (Fall 2018) Solution Set for Homework #7 on Infinite Impulse Response (IIR) Filters CORRECTED EE 33 Linear Signals & Systems (Fall 208) Solution Set for Homework #7 on Infinite Impulse Response (IIR) Filters CORRECTED By: Mr. Houshang Salimian and Prof. Brian L. Evans Prolog for the Solution Set.

More information

Electronic Circuits EE359A

Electronic Circuits EE359A Electronic Circuits EE359A Bruce McNair B26 bmcnair@stevens.edu 21-216-5549 Lecture 22 569 Second order section Ts () = s as + as+ a 2 2 1 ω + s+ ω Q 2 2 ω 1 p, p = ± 1 Q 4 Q 1 2 2 57 Second order section

More information

ELEG 305: Digital Signal Processing

ELEG 305: Digital Signal Processing ELEG 305: Digital Signal Processing Lecture : Design of Digital IIR Filters (Part I) Kenneth E. Barner Department of Electrical and Computer Engineering University of Delaware Fall 008 K. E. Barner (Univ.

More information

Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science. Fall Solutions for Problem Set 2

Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science. Fall Solutions for Problem Set 2 Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science Issued: Tuesday, September 5. 6.: Discrete-Time Signal Processing Fall 5 Solutions for Problem Set Problem.

More information

Filter Banks with Variable System Delay. Georgia Institute of Technology. Atlanta, GA Abstract

Filter Banks with Variable System Delay. Georgia Institute of Technology. Atlanta, GA Abstract A General Formulation for Modulated Perfect Reconstruction Filter Banks with Variable System Delay Gerald Schuller and Mark J T Smith Digital Signal Processing Laboratory School of Electrical Engineering

More information

UNIT - III PART A. 2. Mention any two techniques for digitizing the transfer function of an analog filter?

UNIT - III PART A. 2. Mention any two techniques for digitizing the transfer function of an analog filter? UNIT - III PART A. Mention the important features of the IIR filters? i) The physically realizable IIR filters does not have linear phase. ii) The IIR filter specification includes the desired characteristics

More information

Fourier Series Representation of

Fourier Series Representation of Fourier Series Representation of Periodic Signals Rui Wang, Assistant professor Dept. of Information and Communication Tongji University it Email: ruiwang@tongji.edu.cn Outline The response of LIT system

More information

ELEN E4810: Digital Signal Processing Topic 11: Continuous Signals. 1. Sampling and Reconstruction 2. Quantization

ELEN E4810: Digital Signal Processing Topic 11: Continuous Signals. 1. Sampling and Reconstruction 2. Quantization ELEN E4810: Digital Signal Processing Topic 11: Continuous Signals 1. Sampling and Reconstruction 2. Quantization 1 1. Sampling & Reconstruction DSP must interact with an analog world: A to D D to A x(t)

More information

All-Pole Recursive Digital Filters Design Based on Ultraspherical Polynomials

All-Pole Recursive Digital Filters Design Based on Ultraspherical Polynomials RADIOENGINEERING, VOL. 3, NO. 3, SEPTEMBER 4 949 All-Pole Recursive Digital Filters Design Based on Ultraspherical Polynomials Nikola STOJANOVIĆ, Negovan STAMENKOVIĆ, Vidosav STOJANOVIĆ University of Niš,

More information

Review of Discrete-Time System

Review of Discrete-Time System Review of Discrete-Time System Electrical & Computer Engineering University of Maryland, College Park Acknowledgment: ENEE630 slides were based on class notes developed by Profs. K.J. Ray Liu and Min Wu.

More information

Design of Narrow Stopband Recursive Digital Filter

Design of Narrow Stopband Recursive Digital Filter FACTA UNIVERSITATIS (NIŠ) SER.: ELEC. ENERG. vol. 24, no. 1, April 211, 119-13 Design of Narrow Stopband Recursive Digital Filter Goran Stančić and Saša Nikolić Abstract: The procedure for design of narrow

More information

Design of Biorthogonal FIR Linear Phase Filter Banks with Structurally Perfect Reconstruction

Design of Biorthogonal FIR Linear Phase Filter Banks with Structurally Perfect Reconstruction Electronics and Communications in Japan, Part 3, Vol. 82, No. 1, 1999 Translated from Denshi Joho Tsushin Gakkai Ronbunshi, Vol. J81-A, No. 1, January 1998, pp. 17 23 Design of Biorthogonal FIR Linear

More information

Like bilateral Laplace transforms, ROC must be used to determine a unique inverse z-transform.

Like bilateral Laplace transforms, ROC must be used to determine a unique inverse z-transform. Inversion of the z-transform Focus on rational z-transform of z 1. Apply partial fraction expansion. Like bilateral Laplace transforms, ROC must be used to determine a unique inverse z-transform. Let X(z)

More information

Chapter 7: Filter Design 7.1 Practical Filter Terminology

Chapter 7: Filter Design 7.1 Practical Filter Terminology hapter 7: Filter Design 7. Practical Filter Terminology Analog and digital filters and their designs constitute one of the major emphasis areas in signal processing and communication systems. This is due

More information

Digital Control & Digital Filters. Lectures 21 & 22

Digital Control & Digital Filters. Lectures 21 & 22 Digital Controls & Digital Filters Lectures 2 & 22, Professor Department of Electrical and Computer Engineering Colorado State University Spring 205 Review of Analog Filters-Cont. Types of Analog Filters:

More information

Direct Design of Orthogonal Filter Banks and Wavelets

Direct Design of Orthogonal Filter Banks and Wavelets Direct Design of Orthogonal Filter Banks and Wavelets W.-S. Lu T. Hinamoto Dept. of Electrical & Computer Engineering Graduate School of Engineering University of Victoria Hiroshima University Victoria,

More information

Transform Representation of Signals

Transform Representation of Signals C H A P T E R 3 Transform Representation of Signals and LTI Systems As you have seen in your prior studies of signals and systems, and as emphasized in the review in Chapter 2, transforms play a central

More information

Low-delay perfect reconstruction two-channel FIR/IIR filter banks and wavelet bases with SOPOT coefficients

Low-delay perfect reconstruction two-channel FIR/IIR filter banks and wavelet bases with SOPOT coefficients Title Low-delay perfect reconstruction two-channel FIR/IIR filter banks and wavelet bases with SOPOT coefficients Author(s) Liu, W; Chan, SC; Ho, KL Citation Icassp, Ieee International Conference On Acoustics,

More information

New Design of Orthogonal Filter Banks Using the Cayley Transform

New Design of Orthogonal Filter Banks Using the Cayley Transform New Design of Orthogonal Filter Banks Using the Cayley Transform Jianping Zhou, Minh N. Do and Jelena Kovačević Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign,

More information

PS403 - Digital Signal processing

PS403 - Digital Signal processing PS403 - Digital Signal processing 6. DSP - Recursive (IIR) Digital Filters Key Text: Digital Signal Processing with Computer Applications (2 nd Ed.) Paul A Lynn and Wolfgang Fuerst, (Publisher: John Wiley

More information

EECS 123 Digital Signal Processing University of California, Berkeley: Fall 2007 Gastpar November 7, Exam 2

EECS 123 Digital Signal Processing University of California, Berkeley: Fall 2007 Gastpar November 7, Exam 2 EECS 3 Digital Signal Processing University of California, Berkeley: Fall 7 Gastpar November 7, 7 Exam Last name First name SID You have hour and 45 minutes to complete this exam. he exam is closed-book

More information

Digital Signal Processing Lecture 9 - Design of Digital Filters - FIR

Digital Signal Processing Lecture 9 - Design of Digital Filters - FIR Digital Signal Processing - Design of Digital Filters - FIR Electrical Engineering and Computer Science University of Tennessee, Knoxville November 3, 2015 Overview 1 2 3 4 Roadmap Introduction Discrete-time

More information

The Discrete-Time Fourier

The Discrete-Time Fourier Chapter 3 The Discrete-Time Fourier Transform 清大電機系林嘉文 cwlin@ee.nthu.edu.tw 03-5731152 Original PowerPoint slides prepared by S. K. Mitra 3-1-1 Continuous-Time Fourier Transform Definition The CTFT of

More information

Design of Stable IIR filters with prescribed flatness and approximately linear phase

Design of Stable IIR filters with prescribed flatness and approximately linear phase Design of Stable IIR filters with prescribed flatness and approximately linear phase YASUNORI SUGITA Nagaoka University of Technology Dept. of Electrical Engineering Nagaoka city, Niigata-pref., JAPAN

More information

z-transform Chapter 6

z-transform Chapter 6 z-transform Chapter 6 Dr. Iyad djafar Outline 2 Definition Relation Between z-transform and DTFT Region of Convergence Common z-transform Pairs The Rational z-transform The Inverse z-transform z-transform

More information

Discrete Time Systems

Discrete Time Systems Discrete Time Systems Valentina Hubeika, Jan Černocký DCGM FIT BUT Brno, {ihubeika,cernocky}@fit.vutbr.cz 1 LTI systems In this course, we work only with linear and time-invariant systems. We talked about

More information

Review of Fundamentals of Digital Signal Processing

Review of Fundamentals of Digital Signal Processing Chapter 2 Review of Fundamentals of Digital Signal Processing 2.1 (a) This system is not linear (the constant term makes it non linear) but is shift-invariant (b) This system is linear but not shift-invariant

More information

INFINITE-IMPULSE RESPONSE DIGITAL FILTERS Classical analog filters and their conversion to digital filters 4. THE BUTTERWORTH ANALOG FILTER

INFINITE-IMPULSE RESPONSE DIGITAL FILTERS Classical analog filters and their conversion to digital filters 4. THE BUTTERWORTH ANALOG FILTER INFINITE-IMPULSE RESPONSE DIGITAL FILTERS Classical analog filters and their conversion to digital filters. INTRODUCTION 2. IIR FILTER DESIGN 3. ANALOG FILTERS 4. THE BUTTERWORTH ANALOG FILTER 5. THE CHEBYSHEV-I

More information

Discrete-Time David Johns and Ken Martin University of Toronto

Discrete-Time David Johns and Ken Martin University of Toronto Discrete-Time David Johns and Ken Martin University of Toronto (johns@eecg.toronto.edu) (martin@eecg.toronto.edu) University of Toronto 1 of 40 Overview of Some Signal Spectra x c () t st () x s () t xn

More information

Today. ESE 531: Digital Signal Processing. IIR Filter Design. Impulse Invariance. Impulse Invariance. Impulse Invariance. ω < π.

Today. ESE 531: Digital Signal Processing. IIR Filter Design. Impulse Invariance. Impulse Invariance. Impulse Invariance. ω < π. Today ESE 53: Digital Signal Processing! IIR Filter Design " Lec 8: March 30, 207 IIR Filters and Adaptive Filters " Bilinear Transformation! Transformation of DT Filters! Adaptive Filters! LMS Algorithm

More information

Review of Fundamentals of Digital Signal Processing

Review of Fundamentals of Digital Signal Processing Solution Manual for Theory and Applications of Digital Speech Processing by Lawrence Rabiner and Ronald Schafer Click here to Purchase full Solution Manual at http://solutionmanuals.info Link download

More information

ECE503: Digital Signal Processing Lecture 6

ECE503: Digital Signal Processing Lecture 6 ECE503: Digital Signal Processing Lecture 6 D. Richard Brown III WPI 20-February-2012 WPI D. Richard Brown III 20-February-2012 1 / 28 Lecture 6 Topics 1. Filter structures overview 2. FIR filter structures

More information

EE 225D LECTURE ON DIGITAL FILTERS. University of California Berkeley

EE 225D LECTURE ON DIGITAL FILTERS. University of California Berkeley University of California Berkeley College of Engineering Department of Electrical Engineering and Computer Sciences Professors : N.Morgan / B.Gold EE225D Digital Filters Spring,1999 Lecture 7 N.MORGAN

More information

! Downsampling/Upsampling. ! Practical Interpolation. ! Non-integer Resampling. ! Multi-Rate Processing. " Interchanging Operations

! Downsampling/Upsampling. ! Practical Interpolation. ! Non-integer Resampling. ! Multi-Rate Processing.  Interchanging Operations Lecture Outline ESE 531: Digital Signal Processing Lec 10: February 14th, 2017 Practical and Non-integer Sampling, Multirate Sampling! Downsampling/! Practical Interpolation! Non-integer Resampling! Multi-Rate

More information

LTI Discrete-Time Systems in Transform Domain Ideal Filters Zero Phase Transfer Functions Linear Phase Transfer Functions

LTI Discrete-Time Systems in Transform Domain Ideal Filters Zero Phase Transfer Functions Linear Phase Transfer Functions LTI Discrete-Time Systems in Transform Domain Ideal Filters Zero Pase Transfer Functions Linear Pase Transfer Functions Tania Stataki 811b t.stataki@imperial.ac.uk Types of Transfer Functions Te time-domain

More information

The Approximation Problem

The Approximation Problem EE 508 Lecture 3 The Approximation Problem Classical Approximating Functions - Thompson and Bessel Approximations Review from Last Time Elliptic Filters Can be thought of as an extension of the CC approach

More information

Multiresolution image processing

Multiresolution image processing Multiresolution image processing Laplacian pyramids Some applications of Laplacian pyramids Discrete Wavelet Transform (DWT) Wavelet theory Wavelet image compression Bernd Girod: EE368 Digital Image Processing

More information

Transform analysis of LTI systems Oppenheim and Schafer, Second edition pp For LTI systems we can write

Transform analysis of LTI systems Oppenheim and Schafer, Second edition pp For LTI systems we can write Transform analysis of LTI systems Oppenheim and Schafer, Second edition pp. 4 9. For LTI systems we can write yœn D xœn hœn D X kd xœkhœn Alternatively, this relationship can be expressed in the z-transform

More information

An Iir-Filter Example: A Butterworth Filter

An Iir-Filter Example: A Butterworth Filter An Iir-Filter Example: A Butterworth Filter Josef Goette Bern University of Applied Sciences, Biel Institute of Human Centered Engineering - microlab JosefGoette@bfhch February 7, 2017 Contents 1 Introduction

More information

Wavelets and Filter Banks

Wavelets and Filter Banks Wavelets and Filter Banks Inheung Chon Department of Mathematics Seoul Woman s University Seoul 139-774, Korea Abstract We show that if an even length filter has the same length complementary filter in

More information