Introduction to Audiovisual Processing

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Credits
6
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
TSC
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

Wiener Filter and Adaptive Filter (3 weeks)
- Mean square linear estimation
- Wiener Filter
- Linear regression and Mean square
- Adaptive Filter

Transforms (3 weeks)
- Time Dependent Fourier Transform and 2D
- Discrete Cosine Transform (DCT)
- Karhunen-Loeve Transform (KLT)
- Application to data compression and biometry

Teachers

Person in charge

  • Ferran Marques Acosta ( )

Others

  • Carlos Hernández Pérez ( )
  • Francesc Rey Micolau ( )

Weekly hours

Theory
2.4
Problems
0.9
Laboratory
0.5
Guided learning
0
Autonomous learning
6.2

Competences

Technical Competences

Technical competencies

  • CE5 - Design and apply techniques of signal processing, choosing between different technological tools, including those of Artificial vision, speech recognition and multimedia data processing.

Transversal Competences

Transversals

  • CT6 [Avaluable] - 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.

Basic

  • CB4 - That the students can transmit information, ideas, problems and solutions to a specialized and non-specialized public.
  • CB5 - That the students have developed those learning skills necessary to undertake later studies with a high degree of autonomy

Generic Technical Competences

Generic

  • 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.

Objectives

  1. Know how to characterize stochastic processes
    Related competences: CE5, CG1, CG5, CB4, CB5,
  2. Understand and know how to use the most common signal transforms and their application
    Related competences: CE5, CT6, CG2, CB4, CB5,
  3. To obtain basic optimal and adaptive filtering background for audiovisual data applications
    Related competences: CE5, CT6, CG5, CB4, CB5,

Contents

  1. Statistical Signal modelling
    Stochastic processes: Definition. Autocorrelation. Stationarity, Ergodicity. Power spectral density. Discrete processes. Process filtering.
  2. Estimation Theory
    (1) Parameter Estimation: Concept, quality measures and types of estimators

    (2) Function estimators: Autocorrelation and Power Density Espectral estimation
  3. Optimal filter and adaptive filter
    Types of filters: System identification, equalization, cancellation, prediction and interpolation. Wiener filter. Linear regression and minimum squares. Adaptive filter
  4. Transforms
    Frequency analysis: (1) Discrete Cosinus transform (DCT), (2) Short-time Fourier Transform. Interpretation as a filter bank. Window effect. Reconstruction. Spectrogram.

    Statistical analysis: (1) Periodogram. Estimation principles. (2) Karhunen-Loeve Transform (KLT).

Activities

Activity Evaluation act


Unit 1

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
Objectives: 1
Contents:
Theory
10.3h
Problems
3.7h
Laboratory
2.3h
Guided learning
0h
Autonomous learning
23h

Unit 2

Theory, exercise and laboratory classes corresponding to Unit 2
  • Theory: Theory classes corresponding to Unit 2
  • Problems: Exercise classes corresponding to Unit 2
  • Laboratory: Laboratory classes corresponding to Unit 2
  • Guided learning: Driven learning corresponding to Unit 2
  • Autonomous learning: Autonomous learning corresponding to Unit 2
Objectives: 2
Contents:
Theory
15.4h
Problems
5.6h
Laboratory
3.4h
Guided learning
0h
Autonomous learning
36.5h

Unit 3

Theory, exercise and laboratory classes corresponding to Unit 3
  • Theory: Theory classes corresponding to Unit 3
  • Problems: Exercise classes corresponding to Unit 3
  • Laboratory: Laboratory classes corresponding to Unit 3
  • Guided learning: Driven learning corresponding to Topic 3
  • Autonomous learning: Autonomous learning corresponding to Unit 3
Objectives: 3
Contents:
Theory
10.3h
Problems
3.7h
Laboratory
2.3h
Guided learning
0h
Autonomous learning
23h

Teaching methodology

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.

Evaluation methodology

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+0.25P+0.15L ; 0.85F+0.15L; 0.75F+0.25P; 1.0F)

In the case of a re-evaluation exam (R), the final mark is

Mark = max(0.85R+0.15L; 1.0R)

Bibliography

Basic:

Complementary:

Previous capacities

The knowledge acquired in the subjects of the degree in previous semesters