35340 - Multimedia Digital Signal Processing (2nd cycle)

Learning outcomes

At the end of the course, students will have skills to analyze and design digital signal processing algorithms for energy efficient communications systems. In particular, students will be able to: analyze digital systems in time-domain, frequency-domain, Z-domain; design digital filters; design multirate digital signal processing systems; design adaptive filters. Finally, the student will know the methodologies to analyze and design efficient signal processing blocks taking into account the required energy resources.

Course contents

Discrete-time signals, spectral representation, convolution and correlation operators. Discrete-time linear systems, numerical filters and filtering. Sampling, quantization and reconstruction. Multi-rate systems, decimation, interpolation. Discrete Fourier transform (DFT), fast Fourier transform (FFT), Z transform, discrete cosine transform (DCT). Circular convolution and block filtering through FFT. Design of FIR and IIR filters. Spectral analysis. Linear prediction, estimation, optimal filtering and adaptive filtering. Filter banks. image and video compression (JPEG and MPEG standard), digital audio processing.

Readings/Bibliography

▪    A. V. Oppenheim, R. W. Schafer, Elaborazione Numerica deiSegnali, Franco Angeli, 1996.

▪    M. Bellanger, Digital Processing of Signals, Third Ed. John Wiley & Sons, 2000.

▪    V. K. Ingle, J. G. Proakis, Digital Signal Processing using MATLAB,Brooks/Cole, 2000.

▪    J. G. Proakis, C.M.Rader, F. Ling, C. L. Nikias, et alii, Algorithms for Statistical Signal Processing, Prentice Hall, Upper Saddle River, NJ, 2002.

▪    R. C. Gonzalez, R. E. Woods, Digital Image Processing, Second edition, Prentice Hall, NJ, 2002.

▪    R. C. Gonzalez, R. E. Woods, S. L. Eddins, Digital Image Processing using MATLAB, Prentice Hall, NJ, 2004.

Teaching methods

The lectures are supported by some design examples to solve practical problems in digital signal processing by writing Matlab code: audio source localization, audio reverberation, channel equalization, etc. The code developed in the class is available to the students for further developments.

Assessment methods

Locally developed pre- and post- tests will be used to assess skills in analysing and designing digital signal processing algorithms in real-world applications.

All students will show substantial improvement in stated learning outcomes, as indicated by pre- and post- evaluation of real problems.

Teaching tools

Lectures and exercises are carried out with the help of a personal computer and the Matlab platform freely available to students. There is also a tutorial on audio and video coding techniques (JPEG, MPEG, MP3, etc.).

Links to further information

http://sites.google.com/site/andrewgiorgetti/

Office hours

See the website of Andrea Giorgetti