2018 Multimedia Information Processing

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Academic unit or major
Graduate major in Artificial Intelligence
Shinoda Koichi  Shimosaka Masamichi 
Class Format
Media-enhanced courses
Day/Period(Room No.)
Mon7-8(W631,G311)  Thr7-8(W631,G311)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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, images, and video. It then teaches signal processing and semantic analysis for various mobile sensors which 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.


speech analysis, speech recognition, speech synthesis, image recognition, gesture recognition, video information retrieval, 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 Image analysis Explain in the class.
Class 8 Image recognition Explain in the class.
Class 9 Video analysis Explain in the class.
Class 10 Video understanding Explain in the class.
Class 11 Mobile sensing Explain in the class.
Class 12 Activity recognition Explain in the class.
Class 13 GPS location analytics Explain in the class.
Class 14 Wireless indoor localization Explain in the class.
Class 15 Crowd sensing Explain in the class.


None required.

Reference books, course materials, etc.


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.



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