2024 Faculty Courses School of Computing Department of Mathematical and Computing Science Graduate major in Artificial Intelligence
Multimedia Information Processing
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Koichi Shinoda / Masamichi Shimosaka
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Mon / 7-8 Thu
- Class
- -
- Course Code
- ART.T547
- Number of credits
- 200
- Course offered
- 2024
- Offered quarter
- 2Q
- Syllabus updated
- Mar 14, 2025
- Language
- English
Syllabus
Course overview and goals
Multimedia include many kinds of media, such as audio, speech, still images, video, texts, outputs from various sensors. This course first teaches signal processing, pattern recognition, and information retrieval for speech. It then teaches signal processing and semantic analysis for various mobile sensors that form the Internet of Things (IoT). This course facilitates students' understanding of multimedia technology and development their ability of multilateral ways of thinking.
Course description and aims
At the end of this course, students will be able to explain the multimedia technology and to design a system using multimedia.
Keywords
speech analysis, speech recognition, speech synthesis, speaker recognition, mobile sensor, behavior understanding
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
At the beginning of each class, basic principles and fundamental strategies are explained.
Towards the end of the class, case studies and application examples are introduced.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Speech recognition: overview | Explain in the class. |
| Class 2 | Speech analysis | Explain in the class. |
| Class 3 | DP matching | Explain in the class. |
| Class 4 | Hidden Markov model | Explain in the class. |
| Class 5 | Language Modeling | Explain in the class. |
| Class 6 | Speech recognition system | Explain in the class. |
| Class 7 | Speech recognition using deep learning | Explain in the class. |
| Class 8 | Noise-robust speech recognition | Explain in the class. |
| Class 9 | Speaker recognition | Explain in the class. |
| Class 10 | Mobile sensing | Explain in the class. |
| Class 11 | GPS location data analytics | Explain in the class. |
| Class 12 | Wireless indoor localization | Explain in the class. |
| Class 13 | Designing mobile sensing | Explain in the class. |
| Class 14 | Applying mobile sensing | Explain in the class. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None required.
Reference books, course materials, etc.
None.
Evaluation methods and criteria
Three reports 90% (@30%), exercise (10%)
Related courses
- ART.T463 ： Computer Graphics
- CSC.T421 ： Human Computer Interaction
Prerequisites
Students are required to have the knowledge on computer science of undergraduate levels.
Other
None.