Advanced Digital Signal Processing 3-0-0-3 


  • To provide rigorous foundations in multirate signal processing, power spectrum estimation and adaptive filters.



Review of sampling theory. Sampling rate conversion by integer and rational factors. Efficient realization

and applications of sampling rate conversion.

Wiener filtering. Optimum linear prediction. Levinson- Durbin algorithm. Prediction error filters.

Adaptive  filters.  FIR adaptive  LMS    algorithm.  Convergence  of  adaptive  algorithms.  Fast  algorithms. Applications: Noise canceller, echo canceller and equalizer.

Recursive least squares algorithms. Matrix inversion lemma. Convergence analysis of the RLS algorithm. Adaptive beam forming. Kalman filtering.

Spectrum  estimation.  Estimation  of  autocorrelation.  Periodogram  method.  Nonparametric  methods. Parametric methods.


Text Books

1.   J.G.Proakis, M. Salehi, “Advanced Digital Signal Processing”, McGraw –Hill,1992.

2.   S.Haykin, “Adaptive Filter Theory (3/e)”, Prentice- Hall,1996.


Reference Books

1.   D.G.Manolakis,  V.  K.  Ingle,  and  S.  M.  Kogon  ,”Statistical  and  Adaptive  Signal  Processing”,


2.   S.L.Marple,”Digital Spectral Analysis”,1987.

3.   M.H.Hays,” Statistical Digital Signal Processing and Modeling”, John-Wiley,2001.



Students are able to

CO1: summarize multirate DSP and design efficient digital filters. CO2: construct multi-channel filter banks.

CO3: select linear filtering techniques to engineering problems. CO4: describe the most important adaptive filter generic problems.

CO5: describe the various adaptive filter algorithms.

        CO6: describe the statistical properties of the conventional spectral estimators.