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2003 Course Advanced Digital Signal Processing

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Total No. of Questions : 12] [Total No. of Pages : 3 [3764]-247 P1354 B.E. (Electronics) ADVANCED DIGITAL SIGNAL PROCESSING (2003 Course) (404205) Time : 3 Hours] [Max. Marks : 100 Instructions to the candidates : 1) Answer Q1 or Q2, Q3 or Q4, Q5 or Q6, from Section I and Q7 or Q8, Q9 or Q10, Q11 or Q12 from Section II. 2) Answers to the two sections should be written in separate answer books. 3) Neat diagrams must be drawn wherever necessary. 4) Use electronic pocket calculator is allowed. 5) Assume suitable data, if necessary. SECTION - I Q1) a) Q2) a) b) Obtain the spectral density, autocorrelation and signal energy when x(t) = A sinct. [8] Explain with proof how autocorrelation and energy spectral density form FT pair and also explain Rayleighs energy theorm. [8] OR Explain sampling rate conversion by a rational factor I/D. [6] Design a two stage decimator for the following specifications: D = 100 Passband : 0 F 50 Transitionband : 50 F 55 Input sampling rate : 10,000 Hz Ripple : S1 = 10 1, S2 = 10 3. [10] Q3) a) b) c) Explain basic LMS adaptive algorithm along with a flow chart. Explain practical limitations of the basic LMS algorithm. Explain the main components of adaptive filter. b) [7] [6] [3] OR Q4) a) Explain how adaptive filters are useful for the following applications:[10] i) ii) b) Adaptive Jammer suppression. Adaptive telephone echo cancellation. Explain RLS algorithm and compare its performance with LMS algorithm. [6] P.T.O. Q5) a) Define the following: i) Autoregressive (AR) process. ii) Moving average (MA) process. iii) Autoregressive, moving average (ARMA) process. [6] b) Explain prediction error filter with the help of neat diagram. [6] c) Derive the optimum reflection coefficient for the lattice Forward & Backward predictors. [6] OR Q6) a) Explain the Levinson Durbin algorithm for the solution of the normal equations. [8] b) Explain any two properties of the linear prediction error filters. [6] c) Explain AR Lattice structure. [4] SECTION - II Q7) a) Compare the computational requirements of non parametric power spectrum estimate. [10] b) Determine the mean and autocorrelation of the sequence x(n) generated by the MA(2) process described by difference equation: x(n) = w(n) 2w(n 1) + w(n 2) Where w(n) is a white noise process with variance 2 w. [8] OR Q8) a) Explain with an application how to estimate power spectrum using autoregressive modelling. [10] b) Explain the effect of spectral leakage and spectral smearing with an example. [8] Q9) a) b) Explain SHARC architecture with neat block diagram. State and then discuss four key factors, apart from execution speed, that should be considered in choosing a DSP processor for each of the following application. [6] i) High fidelity digital audio. ii) [3764]-247 [10] Physiological signal processing for diagnosis. 2 OR Q10)a) Explain how Harvard architecture as used by TMS 320 family differs from the strict Harvard architecture. Compare this with the architecture of a standard Von Neumann processor. [8] b) Explain the concept of pipelining with an example and appropriate timing diagram. [8] Q11)a) Explain the speech production mechanism with the help of a neat diagram. [8] b) What is formant? [2] c) Draw the block diagram of formant synthesizer and explain. [6] OR Q12)a) Explain the LPC model of speech. [8] b) Explain the difference between Vowel and Consonant. [4] c) Define the following terms: [4] i) Bilabial. ii) Glottal. Y [3764]-247 3

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