K-INFO
HU
EN
Login

Media Informatics Systems

Médiainformatikai rendszerek
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
Subject name (Hungarian, English)
Médiainformatikai rendszerek
Media Informatics Systems
Subject code BMEVITMMA08
Subject type
Training Level
Course types and hours (weekly/semester)
Course type lecture tutorial laboratory
hours (weekly) 2 1 0
type (linked/independent) derived course
Assessment type vizsga
Credits 4
Subject coordinator
DR. Mihajlik Péter
Responsible department
Távközlési és Mesterséges Intelligencia Tanszék
Faculty Villamosmérnöki és Informatikai Kar
Subject website
Primary curriculum type
Direct prerequisites – Strong prerequisite none
Direct prerequisites – Weak prerequisite none
Direct prerequisites – Parallel prerequisite none
Direct prerequisites – Milestone prerequisite none
Direct prerequisites – Exclusion none

Objectives

Programme

 

Week 1:

 

Basic definitions. Media content management. Main topics of multimedia (image, voice, video) processing.

 

Week 2:

 

Processing audio contents. Short Time Fourier Spectrum, windowing, spectrogram computation. The fundamentals of signal detection.

 

Week 3:

 

Music recognition. Challenges in real-time pattern matching: additive noise, linear and non-linear distortions. Audio fingerprinting. Case study: Shazam.

 

Week 4:

 

Recognition of variable sound and image signals. Statistics based general classification. Probability density function, likelihood, training and test. Bayes' theorem.

 

Week 5:

 

Multimedia classification tasks based on multi-variate Gaussian Mixture Models.

 

Week 6:

 

Maximum Likelihood vs. Discriminative Training – theoretical and practical issues regarding audio and image data. The application of Multi Layer Perceptrons on media informatics tasks.

 

Week 7:

 

Processing time-varying signals: Dynamic Time Warping, Hidden Markov-Models and their practical implementations. Fundamentals of speech recognition.

 

Week 8:

 

Contemporary speech recognition technologies. Acoustic, pronunciation and language models. Subtitling methods and standards. Case studies: BBC and the Hungarian National Television subtitling approaches.

 

Week 9:

 

Deep learning: deep feed-forward neural nets, convolutional nets and requrrent networks - and their applications on automatic annotation of media contents (text, sound, image and video).

 

Week 10:

 

Complex tasks based on multimedia technologies: shape detection in images, object tracking in videos. Face detection and recognition solutions. Video processing methods in practice: hand gesture recognition in videos.

 

Week 11:

 

Metadata: semantic and desriptive metadata. Multimedia metadata standards. EBU/SMPTE metadata, Dublin Core, Material Exchange Format (MXF).

 

Week 12:

 

Multimedia databases. Multimedia information retrieval. Search modes, types, algorithms. Quality measurement of an information retrieval system.

 

Week 13:

 

Digital Media Management Systems (DMMS) / Multimedia Asset Management. Structure of the systems: gathering, storing, displaying subsystems. Lifecycle attributes. Integration tools. Architecture of the media content management systems, and types of it: DAM, DM, KM, Web CMS, ECM. Overall model of systems.

 

Week 14:

 

Digital archiving: tasks, approaches, technics. Archiving strategies: On-line, near-line, off-line, off-site accessibility, levels. Record management.

 

In the practice sessions, the following topics (not exculsively and not fully) will be discussed: audio feature extraction, audio fingerprinting, GMM, automatic speech recognition, language modeling, applied deep neural nets in Keras, image annotation tasks, multimedia retrieval, visualization tools, video processing.

The aim of the course is to present the fundamentals of digital multimedia content management and to introduce the applied pattern recognition and analytic techniques. The students learn about the recognition and categorization issues and the standards of desriptive attributes of multimedia contents. At the end of the semester the student will be able to understand and accomplish engineering tasks related to media informatics systems by acquiring the required technologies and tools. 

Learning outcomes

Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:

Knowledge

No learning outcomes recorded.

Skills

No learning outcomes recorded.

Attitudes

No learning outcomes recorded.

Autonomy and responsibility

No learning outcomes recorded.

Oktatási módszertan

2×45 min lecture and 1×45 min seminar per week (90 minutes biweekly).

Tanulástámogató anyagok

Online források
David Austerberry: Digital Asset Management, FocalPress, 2006.; Serkan Kiranyaz, Moncef Gabbouj: Content-Based Management of Multimedia Databases: Advanced Techniques for Multimedia Analysis and Retrieval, LAP LAMBERT Academic Publishing, 2012.; Altrichter Márta, Horváth Gábor, Pataki Béla, Strausz György, Takács Gábor, Valyon József: Neurális hálózatok, Hungarian Edition Panem Könyvkiadó Kft., Budapest, 2006  ; Michael Nielsen: Neural Networks and Deep Learning, 2016.; Online: http://neuralnetworksanddeeplearning.com/; Rabiner, L., Juang, B-H., Fundamentals of Speech Recognition. Prentice Hall, New Jersey, 1993

Recommended preliminary knowledge for completing the subject

Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
Mediainformation technologies and tools
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
Mediainformation technologies and tools
General rules
Requirements: Requirements: - Mid-term (written) test Exam period: - Exam Additional possibilities: There is one possibility to repeat the test (Mid-term) in the teaching period and there is a final one in the official recap period.
Assessment methods
In-term assessments

No detailed assessments provided.

Weight of in-term assessments

No weights provided.

Exam-period assessments

No detailed assessments provided.

Weight of exam elements

No weights provided.

Grade calculation

No grade thresholds provided.

Attendance requirements

No attendance requirements provided.

Rules for retake and resubmission

Not provided.

Short description

Not provided.

Detailed description

Not provided.

Recommended courses

Not provided.

Workload to complete the subject

No workload breakdown provided.

Validity of subject requirements
Requirements valid from:
Requirements valid until:
Curriculum placement

No curriculum placements recorded for this subject version.