Wang: Computational Audition Course Syllabus
Dragon Star Short Course (Summer 2013)
Computational Audition
Syllabus
Time: Monday through Friday, July 22 – July 26, 2013
Lectures: 8:30-11:30
Discussions: 1:30-3:30
Place: TBA, Shanghai Jiaotong University, Shanghai
Instructor: Prof. DeLiang Wang; Email: dwang@cse.ohio-state.edu
URL http://www.cse.ohio-state.edu/~dwang
Host: Dr. Kai Yu, Shanghai Jiaotong University, Kai.yu@sjtu.edu.cn
Course Description:
A graduate-level introduction to fundamental concepts and algorithms of computational
audition. Topics include basics of audition, pitch analysis, sound localization, auditory scene
analysis, speech and music processing (enhancement, segregation, and transcription), speech and
speaker recognition, and modeling of the auditory system.
Course Objectives:
Upon completion of the course, the participant will have gained:
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Understanding physiological/psychoacoustic phenomena of audition
Comprehension of theories, models, and algorithms in computational audition
Ability to solve practical problems in audio and speech processing
Course Material:
Lecture notes plus selected papers from the literature.
All the materials can be downloaded from http://bcmi.sjtu.edu.cn/dragon-star/resources.html
Evaluation:
A project within the scope of the short course, and a summary description of the project
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Wang: Computational Audition Course Syllabus
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Wang: Computational Audition Course Syllabus
Tentative Schedule
Day
Topics
Monday
Introduction and background
 Marrian information processing framework
 Acoustics
 Signal processing
Tuesday
Physiological and psychoacoustic basis of audition
 Physiological basis
 Psychoacoustics
 Auditory scene analysis
 Real-world audition
Wednesday
Fundamentals of computational audition
 Auditory representations
 Multipitch tracking
 Auditory segmentation
 Ideal binary mask as CASA goal
Thursday
Computational auditory scene analysis
 Sound separation problem
 Traditional CASA methods
 Classification-based methods
o Binaural segregation
Friday
Pattern recognition and learning
 Automatic speech recognition
 Learning methods: MLP, SVM, and DNN
 Overall discussion: Neural mechanisms of audition
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