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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
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- School of Science
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- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
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- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Shinoda Koichi Shimosaka Masamichi
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Mon7-8() Thr7-8()
- Group
- -
- Course number
- ART.T547
- Credits
- 2
- Academic year
- 2021
- Offered quarter
- 2Q
- Syllabus updated
- 2021/3/19
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
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.
Student learning outcomes
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 that will be developed
| ✔ 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/Required learning
| Course schedule | Required learning | |
|---|---|---|
| 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 | Activity recognition | Explain in the class. |
| Class 12 | GPS location analytics | Explain in the class. |
| Class 13 | Wireless indoor localization | Explain in the class. |
| Class 14 | Crowd sensing | Explain in the class. |
Out-of-Class Study Time (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.
Assessment criteria and methods
Three reports 90% (@30%), exercise (10%)
Related courses
- ART.T463 ： Computer Graphics
- CSC.T421 ： Human Computer Interaction
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Students are required to have the knowledge on computer science of undergraduate levels.
Other
None.
