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: 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 1 Wang: Computational Audition Course Syllabus 2 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 3