Introduction to Audiovisual Processing

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

Others

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 mark: 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