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Posted By: rama Member Level: Silver Posted Date: 10 Jun 2008
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2008 Anna University B.E Electronics and Communication EC1004 ADVANCED DIGITAL SIGNAL PROCESSING Question paper
EC1004 ADVANCED DIGITAL SIGNAL PROCESSING
AIM
To introduce the student to advanced digital signal processing techniques.
OBJECTIVES
• To study the parametric methods for power spectrum estimation.
• To study adaptive filtering techniques using LMS algorithm and to study the applications of adaptive filtering.
• To study multirate signal processing fundamentals.
• To study the analysis of speech signals.
• To introduce the student to wavelet transforms.
UNIT I PARAMETRIC METHODS FOR POWER SPECTRUM ESTIMATION 9
Relationship between the auto correlation and the model parameters – The Yule – Walker method for the AR Model Parameters – The Burg Method for the AR Model parameters – unconstrained least-squares method for the AR Model parameters – sequential estimation methods for the AR Model parameters – selection of AR Model order.
UNIT II ADAPTIVE SIGNAL PROCESSING 9
FIR adaptive filters – steepest descent adaptive filter – LMS algorithm – convergence of LMS algorithms – Application: noise cancellation – channel equalization – adaptive recursive filters – recursive least squares.
UNIT III MULTIRATE SIGNAL PROCESSING 9
Decimation by a factor D – Interpolation by a factor I – Filter Design and implementation for sampling rate conversion: Direct form FIR filter structures – Polyphase filter structure.
UNIT IV SPEECH SIGNAL PROCESSING 9
Digital models for speech signal : Mechanism of speech production – model for vocal tract, radiation and excitation – complete model – time domain processing of speech signal:- Pitch period estimation – using autocorrelation function – Linear predictive Coding: Basic Principles – autocorrelation method – Durbin recursive solution.
UNIT V WAVELET TRANSFORMS 9
Fourier Transform : Its power and Limitations – Short Time Fourier Transform – The Gabor Transform - Discrete Time Fourier Transform and filter banks – Continuous Wavelet Transform – Wavelet Transform Ideal Case – Perfect Reconstruction Filter Banks and wavelets – Recursive multi-resolution decomposition – Haar Wavelet – Daubechies Wavelet.
TOTAL : 45
TEXTBOOKS
1. John G.Proakis, Dimitris G.Manobakis, Digital Signal Processing, Principles, Algorithms and Applications, Third edition, (2000) PHI.
2. Monson H.Hayes – Statistical Digital Signal Processing and Modeling, Wiley, 2002.
REFERENCES
1. L.R.Rabiner and R.W.Schaber, Digital Processing of Speech Signals, Pearson Education (1979).
2. Roberto Crist, Modern Digital Signal Processing, Thomson Brooks/Cole (2004)
3. Raghuveer. M. Rao, Ajit S.Bopardikar, Wavelet Transforms, Introduction to Theory and applications, Pearson Education, Asia, 2000.
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