Multimedia Information Processing
TeachersNAGATA TOMOHIROStaffInfo
Grade, SemesterYear 3 1st semest [Department of Information and Electronic Engineering, Faculty of Science and Engineering]
CategorySpecial Subjects
Elective, CreditsElective 2credit
 Syllabus Number3D326

Course Description

This course provides you the knowledge about multimedia computing technology which integrates various media such as image, sound, speech, and text. You will learn basic method, theory, and element technology to handle sound, speech, and text with computer. Moreover, you will learn basic method and theory of pattern recognition for handling these element media technologies.
You will deepen your understanding of the general principles of the multimedia processing, by actually applying some media processing algorithms to a media to compare and confirm those effects.
The course includes lectures, assignments, and final written examination.
This course follows DP4M in the diplomat policies of the faculty.

Course Objectives

By the end of this course, you will be able to:
(1) understand and explain the fundamental principle and the algorithm of some simple pattern recognition algorithms
(2) understand and explain the Image media processing technology
(3) understand and explain the sound and speech media processing technology
(4) understand and explain the sound and text media processing technology

Grading Policy

Grade will be computed as follows:
- Weekly assignments 50%
- Final examination 50%
You can receive a credit if you obtain 60% and above.

Textbook and Reference

KindTitleAuthorPublisher
Textbookマルチメディアコンピューティング尾内理紀夫コロナ社 (ISBN: 978-4-339-02434-0)
Referencesマルチモーダルインタラクション榎本美香, 飯田仁, 相川清明コロナ社 (ISBN: 978-4-339-02784-6)
References音声言語処理と自然言語処理中川聖一(編)コロナ社 (ISBN: 978-4-339-02888-1)
References音声工学板橋秀一森北出版 (ISBN: 978-4-627-82811-7)

Requirements(Assignments)

Before class:
You should summarize a note named "worksheet" by reading the textbook or additional texts on LMS, and submit it at the beginning of each class.

After class:
At the end of each class, problem sets will be provided over LMS. Solve problem sets for each class and submit them by the deadline.

Note

Schedule

1guidance for this course.
What is multimedia?
2Pattern recognition (1): Bayes decision rule
3Pattern recognition (2): Simple perceptron
4Pattern recognition (3): Support Vector Machine
5Pattern recognition (4): Unsupervised learning
6Speech processing (1): Foundation of sound, Fourier transform
7Speech processing (2): Spectral analysis for speech
8Speech processing (3): Acoustic features
9Speech processing (4): Acoustic analysis for speech recognition
10Speech processing (5): Acoustic analysis for speech synthesis
11Text processing (1): Morphological analysis
12Text processing (2): N-gram
13Text processing (3): Term weighting
14Text processing (4): Information retrieval
15Review and summary