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2003 Course Advanced Digital Signal Processing

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Total No. of Questions : 12] P1475 [Total No. of Pages :3 [3764] - 227 B.E. (E & TC) ADVANCED DIGITAL SIGNAL PROCESSING (2003 Course) (404218) Time : 3 Hours] [Max. Marks:100 Instructions to the candidates: 1) Answers to the two sections should be written in separate books. 2) Neat diagrams must be drawn wherever necessary. 3) Figures to the right indicate full marks. 4) Your answers will be valued as a whole. 5) Use of logarithmic tables slide rule, Mollier charts, electronic pocket calculator and steam tables is allowed. 6) Assume suitable data, if necessary. 7) Solve Q.1 or Q.2, Q.3 or Q.4, Q.5 or Q.6, from section I.solve Q.7 or Q.8, Q.9 or Q.10, Q.11 or Q.12, from section II . SECTION - I Q1) a) b) Obtain the power spectrum of the autocorrelation rx(k) = |k|, where | | < 1. [8] n For a factor of L Up-sampler, governed by xu(n) = x for n = 0, + L, L + 2L,......obtain the Z-Transform equivalent relationship. [8] OR Q2) a) Define Wide Sense Stationary (WSS) process. State & explain any 3 important catagorieal properties. [8] b) It is required to decimate a signal by reducing the sampling rate from 12kHz to 400 Hz. The specifications for H(z) are fp = 180 Hz, fs = 200 Hz, p = 0.002, s = 0.001. Apply H(z) = I(z) F(z6) & compute complexity. [8] Q3) a) Define and explain i) AR ii) ARMA iii) MA processes. For which type of process is the Toeplitz symmetry useful? [10] b) Explain what is an innovations process with the help of a neat sketch.[8] P.T.O. OR Q4) a) What do you mean by forward prediction? Explain how Levinson Durbin Algorithm is vital in the prediction. [10] b) Given a 3-stage lattice filter with coefficients as K1 = 0.8, K2 = 0.5 & K3 = 0.14, determine the FIR filter coefficients with the direct form of structure. [8] Q5) a) What is the role played by Eigen values & Eigen vectors in adaptive filters? Explain in detail. [8] b) Draw the block diagram of a typical adaptive filter. Explain the terms presented. [8] OR Q6) a) b) Explain the following terms for a typical adaptive filter i) Rate of convergence. ii) Misadjustment. iii) Tracking. [8] Highlight & explain major differences in RLS & LMS adaptive algorithms. [8] SECTION - II Q7) a) b) Explain the salient features of Bartlett s method of power spectral density [8] estimation. Explain in detail the following terms with a neat sketch : i) Periodogram. ii) Correlogram. [8] OR Q8) a) Define the parametric & non-parametric methods of power spectral estimation. Highlight the major differences between them. Justify under what condition, a certain method is useful. [8] b) Explain the salient features of Welch s method of power spectral density estimation. [8] [3764]-227 2 Q9) a) Explain what are the fixed point & floating point DSP processors. Enlist their application areas. [8] b) What do you mean by pipelining in the context of DSP? Explain with the help of a neat sketch & suitable example. [10] OR Q10)Explain the following terms for a typical DSP processor (any 3) : [18] a) Zero overhead looping. b) Circular buffering. c) Barrel shifter & it s role. d) Super Harvard architecture. Q11)a) Explain with the help of a neat sketch, various steps in digitisation of speech signal. [8] b) State various speech coding techniques & explain any one of them. [8] OR Q12)Explain the following terms in the context of speech processing (any 4) :[16] a) Cepstrum. b) Pitch & pitch detection. c) Formants. d) Signal detection. e) Vowels, consonants & nasals. kbkb [3764]-227 3

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