Title :
Reinforcement Learning of Listener Response for Mood Classification of Audio
Author :
Stockholm, Jack ; Pasquier, Philippe
Author_Institution :
Sch. of Interactive Arts & Technol., Simon Fraser Univ., Surrey, BC, Canada
Abstract :
This paper describes a method of applying a reinforcement learning artificial intelligence to categorize audio files by mood based on listener response during a performance. The system discussed is implemented in a performance art environment designed to present the moods of multiple participants simultaneously in a room via a diffusion o frepresentative audio samples.
Keywords :
audio signal processing; learning (artificial intelligence); signal classification; artificial intelligence; audio files; listener response; mood classification; reinforcement learning; Art; Artificial intelligence; Dictionaries; Investments; Learning; Lifting equipment; Mood; Portable computers; Artificial Intelligence; Auditory Display; Computer Music; Net Art; Reinforcement Learning;
Conference_Titel :
Computational Science and Engineering, 2009. CSE '09. International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4244-5334-4
Electronic_ISBN :
978-0-7695-3823-5
DOI :
10.1109/CSE.2009.184
