Review of Fundamentals of Digital Signal Processing

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1 Solution Manual for Theory and Applications of Digital Speech Processing by Lawrence Rabiner and Ronald Schafer Click here to Purchase full Solution Manual at Link download full: 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 (since the modulation term is not shift-invariant) (c) This system is not linear (because of the cubic power) but is shift-invariant (d) This system is linear and shift invariant (in fact the digital system is the convolution of the input with a rectangular window of length N samples. 2.2 (a) The system y[n] = x[n]+2x[n+1]+3 is not linear, as seen by the following counter example. Consider inputs x 1 [n] and x 2 [n] with outputs: y 1 [n] = T [x 1 [n]] = x 1 [n] + 2x 1 [n + 1] + 3 y 2 [n] = T [x 2 [n]] = x 2 [n] + 2x 2 [n + 1] + 3 If we apply the system to the input x 3 [n] = ax 1 [n] + bx 2 [n] we get an output, y 3 [n] of the form: y 3 [n] = T [ax 1 [n] + bx 2 [n]] = [ax 1 [n] + bx 2 [n]] + [2ax 1 [n + 1] + 2bx 2 [n + 1]] + 3 which is not equal to the linear sum ay 1 [n] + by 2 [n] thereby showing that the system is not linear. (b) The system y[n] = x[n]+2x[n+1]+3 is time-invariant (shift-invariant) as seen by considering the responses to x[n] and to x[n n 0 ], i.e., y[n] = T [x[n]] = x[n] + 2x[n + 1] + 3 y[n n o ] = T (x[n n 0 ]) = x[n n 0 ] + 2x[n n 0 + 1] + 3 (c) The system y[n] = x[n] + 2x[n + 1] + 3 is not causal since the output at time n depends on the output at a future time n (a) The input sequence a n is an eigen-function of LTI systems. Therefore if this system is LTI, the output must be of the form A input or y[n] = Aa n where A is a complex constant. Since b n Aa n for any complex constant A, the system cannot be LTI. 3

2 4 CHAPTER 2. REVIEW OF FUNDAMENTALS OF DIGITAL SIGNAL PROCESSING (b) The system is not LTI. (c) In this case, the input excites the system at all frequencies (since it exists only for n 0). Therefore the system transfer function describes how the system transforms all inputs. Thus this system could be LTI and there is only one LTI system with the given transfer function, i.e., H(z) = 1 az 1, z > b 1 bz (a) x[n] can be written in the form: where x[n] = a n u[n n 0 ] u[n n 0 ] = { 1 n n 0 0 n < n 0. We can now solve for X(z) by the following steps: x[n] = a n n0+n0 u[n n 0 ] = a n0 a n n0 u[n n 0 ] X(z) = a n0 n=n 0 a (n n0) z n We can now make a change of variables to the form: n = n n 0 giving: where we have used the relation: X(z) = a n0 a n z n n 0 n =0 = a n0 z n0 a n z n n =0 = an0 z n0 1 az 1 ; az 1 < 1 r n = 1 1 r ; r < 1 n=0 (b) X(e jω ) = X(z) evaluated on the unit circle (i.e., for z = e jω ). Thus we get: X(e jω ) = an0 e jωn0 1 ae jω ; ae jω < 1, or equivalently, a < 1. The Fourier transform exists when the z-transform converges in a region including the unit circle; in this case when a < (a) x[n] can be written in the form: x[n] = x 1 [n] + x 2 [n]

3 5 where and x 1 [n] = u[n] x 2 [n] = u[n N] = x 1 [n N] Using this form for x[n], we can solve for the convolution output as the output due to u[n] for the region 0 n N 1, and as the output due to u[n] u[n N] for the region N n. We call the output of the convolution of x 1 [n] with h[n] as y 1 [n] and we solve for its value in the region 0 n N 1 using the convolution formula: y 1 [n] = u 1 [n] h[n] = x 1 [m]h[n m] = m= n a n m (1) m=0 = a n n m=0 a m = a n 1 a n 1 1 a 1 = 1 an+1 1 a We trivially solve for y 2 [n] = y 1 [n N] as 0 n N 1 y 2 [n] = (1 an+1 ) (1 a) N n giving, for y[n], the value (for the region N n) y[n] = y 1 [n] + y 2 [n] = 1 an+1 1 a 1 an N+1 1 a or, equivalently, y[n] = a n (a N+1 a) (1 a) N n (b) Using z transforms we solve for Y (z) again as a sum in the form: Y (z) = X(z) H(z) = X 1 (z) H(z) + X 2 (z) H(z) where X 1 (z) and X 2 (z) are the z transforms, respectively, of x 1 [n] and x 2 [n] of part (a) of this problem. The resulting set of z transforms is: X 1 (z) = 1 1 z 1 X 2 (z) = z N 1 z 1 1 H(z) = 1 az 1

4 6 CHAPTER 2. REVIEW OF FUNDAMENTALS OF DIGITAL SIGNAL PROCESSING We can now solve for Y 1 (z) = X 1 (z) H(z) giving the form: Y 1 (z) = 1 (1 z 1 )(1 az 1 ) Using the method of partial fraction expansion we factor Y 1 (z) into Y 1 (z) = A 1 z 1 + B 1 az 1 We can now solve for A and B using the fact that the combined numerator is 1, giving: A = 1 1 a, B = a 1 a Now we can invert the partial fraction expansion, giving y 1 [n] = 1 1 a u[n] a 1 a an u[n] = 1 an+1 1 a u[n] which is valid for all n but applies to the region 0 n N 1. Similarly we can trivially solve for y 2 [n] = y 1 [n N]u[n N] again giving the same total result for the region N n. 2.6 (1) The z transform of the exponential window is of the form: N 1 W 1 (z) = a n z n = (1 an z N ) (1 az 1 ) n=0 Note that zeros occur at z k = ae j2πk/n, k = 1, 2,..., N 1. The Fourier transform for the exponential window is just: W 1 (e jω ) = (1 an e jωn ) 1 ae jω ) (2) The rectangular window is a special case of the exponential window with a = 1. The zeros are now all on the unit circle at z k = e j2πk/n, k = 1, 2,..., N 1. The Fourier transform of the rectangular window is of the form: W 2 (e jω ) = (1 ejωn ) (1 e jω ) = e jω(n 1)/2 sin(ωn/2) sin(ω/2) (3) The Hamming window can be expressed in terms of shifted sums of rectangular windows, i.e., w 3 [n] = 0.54w 2 [n] 0.23w 2 [n]e j2/(n 1) 0.23w 2 [n]e j2/(n 1) The Fourier tranform of the Hamming window is thus of the form: W 3 (e jω ) = 0.54W 2 (e jω ) which can be put into the form 0.23W 2 (e j(ω 2π/(N 1) ) 0.23W 2 (e j(ω+2π/(n 1) ) W 3 (e jω ) = e jω(n 1)/2 [ 0.54 sin(ωn/2) sin(ω/2) sin[(ω 2π/(N 1))(N/2)] sin[(ω 2π/(N 1))(1/2)] sin[(ω + 2π/(N 1))(N/2)] sin[(ω + 2π/(N 1))(1/2)] ]

5 7 Figure P2.6.1: Magnitude response of rectangular window Figure P2.6.2: Magnitude response of exponential window Figure P2.6.3: Magnitude response of Hamming window A plot of W2 (ejω ) is shown in Figure P Notice that W1 (z) has zeros on the unit circle; whereas W1 (z) has zeros on a circle of radius a as seen in Figure P Finally the Hamming window magnitude response is shown in Figure P We see how the side lobes tend to cancel and that the main lobe is about twice the width of the rectangular window response. 2.7 (a) A plot of an N = 9-point triangular window is shown in Figure P Figure P2.7.1: 9-point triangular window (b) An N point triangular window can be created by convolving an (N +1)/2-point rectangular window with itself, i.e., wt [n] = wr [n] wr [n].

6 8 CHAPTER 2. REVIEW OF FUNDAMENTALS OF DIGITAL SIGNAL PROCESSING Since convolution in time is equivalent to multiplication in frequency,we have: W T (e jω ) = [ W R (e jω ) ] 2 W R (e jω ) = 1 e jω(n+1)/2 jω(n 1)/4 sin[ω(n + 1)/4] 1 e jω = e sin(ω/2) W T (e jω ) = e jω(n 1)/2 [ sin[ω(n + 1)/4] sin(ω/2) (c) Plots of the time and frequency (log magnitude) responses of a 101-point triangular window are shown in Figure P ] 2 Figure P2.7.2: Time and frequency responses of 101-point triangular window (d) The rectangular, Hamming and triangular windows compare as follows: 1. rectangular window: cutoff frequency= 1/N in normalized frequency units and is F s /N in analog frequency units, with sidelobe rejection 14 db 2. Hamming window: cutoff frequency= 2/N in normalized frequency units and is 2F s /N in analog frequency units, with sidelobe rejection 44 db 3. triangular window: cutoff frequency= 2/N in normalized frequency units and is 2F s /N in analog frequency units, with sidelobe rejection 28 db 2.8 (a) The impulse response of the ideal lowpass filter is obtained as: h[n] = 1 π 2π = 1 2π = sin(ω cn) (b) if ω c = π/4 then h[n] = 1 sin(/4) 4 /4 ωc H(e jω )e jωn dω = 1 e jωn dω π 2π ω c [ ] e jωn ωc = 1 jn ω c 2πjn [ejωcn e jωcn ] and a plot of h[n] is as shown in Figure P2.8.1.

7 Click here to Purchase full Solution Manual at 9 Figure P2.8.1: Impulse response of ideal lowpass filter. Figure P2.8.2: Parallel combination of ideal filters. (c) One apporach to obtaining the desired impulse response is to view H(e jω ) as a parallel combination of lowpass, highpass, and zero phase filters, as shown in Figure P We can express H HP (e jω ) in terms of a lowpass filter with cutoff frequency π ω b with the passband shifted by π. From Part (a) we have: where ω a is the cutoff, giving: where π ω b is the cutoff frequency. The allpass has the property: H LP (e jω ) sin(ω an) H HP (e jω ) = H LP (e j(ω π) ) e j sin[(π ω b)n] H AP (e jω ) = 1 δ[n] Putting it all together we get: h[n] = δ[n] sin(ω an) = δ[n] sin(ω an) e j sin[(π ω b)n] ( 1) n sin[(π ω b)n]

8 10 CHAPTER 2. REVIEW OF FUNDAMENTALS OF DIGITAL SIGNAL PROCESSING (d) When ω a = π/4 and ω b = 3π/4, we can express h[n] for the bandpass filter as: 0.25 sin(/4) h[n] = δ[n] / sin(/4) = δ[n] /4 = δ[n] 0.25 sin(/4) [1 + ( 1) n ] /4 n sin[(π 3π/4)n] ( 1) n 0.25 sin(/4) ( 1) /4 A plot of h[n] for the bandpass filter is shown in Figure P Figure P2.8.3: Impulse response of ideal bandpass filter. 2.9 (a) The magnitude response of an ideal differentiator is: H(e jω ) = ω and the phase response is: arg H(e jω ) = { ωτ + π/2 ω > 0 ωτ π/2 ω < 0 A plot of the magnitude and phase is given in Figure P Figure P2.9.1: Magnitude and phase responses of ideal differentiator. Click here to Purchase full Solution Manual at