Media Informatics Systems
A tantárgyleírás hatályossága
| Subject name (Hungarian, English) |
Médiainformatikai rendszerek
Media Informatics Systems
|
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subject code | BMEVITMMA08 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
|
||||||||||||
| Assessment type | vizsga | ||||||||||||
| Credits | 4 | ||||||||||||
| Subject coordinator |
DR. Mihajlik Péter
contact:
mihajlik.peter@vik.bme.hu
|
||||||||||||
| 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
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.
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
Tanulástámogató anyagok
Online források
Recommended preliminary knowledge for completing the subject
General rules
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
Curriculum placement
No curriculum placements recorded for this subject version.