Statistical Signal Modelling (4 weeks)
- Introduction and Random Variable
- Memoryless process model
- Discrete random processes
Estimation Theory (4 weeks)
- Parameter Estimation: Concept, quality measures and types of estimators
- Function estimators: Autocorrelation and Power Density Espectral estimation
Transforms (3 weeks)
- Time Dependent Fourier Transform and 2D
- Discrete Cosine Transform (DCT)
- Karhunen-Loeve Transform (KLT)
- Application to data compression and biometry
Wiener Filter and Adaptive Filter (3 weeks)
- Mean square linear estimation
- Wiener Filter
- Linear regression and Mean square
- Adaptive Filter
Person in charge
Ferran Marques Acosta (
Francesc Rey Micolau (
Javier Ruiz Hidalgo (
CE5 - Design and apply techniques of signal processing, choosing between different technological tools, including those of Artificial vision, speech recognition and multimedia data processing.
CT6 - Autonomous Learning. Detect deficiencies in one's own knowledge and overcome them through critical reflection and the choice of the best action to extend this knowledge.
CB5 - That the students have developed those learning skills necessary to undertake later studies with a high degree of autonomy
Generic Technical Competences
CG1 - To design computer systems that integrate data of provenances and very diverse forms, create with them mathematical models, reason on these models and act accordingly, learning from experience.
CG2 - Choose and apply the most appropriate methods and techniques to a problem defined by data that represents a challenge for its volume, speed, variety or heterogeneity, including computer, mathematical, statistical and signal processing methods.
CG5 - To be able to draw on fundamental knowledge and sound work methodologies acquired during the studies to adapt to the new technological scenarios of the future.
Know how to characterize stochastic processes
Understand and know how to use the most common signal transforms and their application
To obtain basic optimal and adaptive filtering background for audiovisual data applications
Statistical Signal modelling
Stochastic processes: Definition. Autocorrelation. Stationarity, Ergodicity. Power spectral density. Discrete processes. Process filtering.
(1) Parameter Estimation: Concept, quality measures and types of estimators
(2) Function estimators: Autocorrelation and Power Density Espectral estimation
Frequency analysis: (1) Discrete Cosinus transform (DCT), (2) Short-time Fourier Transform. Interpretation as a filter bank. Window effect. Reconstruction. Spectrogram.
Optimal filter and adaptive filter
Types of filters: System identification, equalization, cancellation, prediction and interpolation. Wiener filter. Linear regression and minimum squares. Adaptive filter
Theory, exercise and laboratory classes corresponding to Unit 1
Theory: Theory classes corresponding to Unit 1
Problems: Exercise classes corresponding to Unit 1
Laboratory: Laboratory classes corresponding to Unit 1
Guided learning: Driven learning corresponding to Unit 1
Autonomous learning: Autonomous learning corresponding to Unit 1
The subject is based on classroom theory classes, problems and laboratory. The theory classes follow the program defined in this teaching guide. Within the lectures, the dialogue between professors and students is promoted by proposing exercises and activities to be carried out jointly based on particular aspects of the topic being dealt with. The laboratory classes exemplify the contents developed in the theory classes.
The final mark is obtained from the partial marks:
- Mid-term exam: M (25%)
- Final exam: F (60%)
- Laboratory assigments: L (15%)
Mark = max (0.6F+0.25M+0.15L ; 0.85F+0.15L)
In the case of a re-evaluation exam (R), the final mark is