Allthatsounds: associative semantic categorization of audio data
Loading...
Author(s)
Rubisch, Julian
Husinsky, Matthias
Raffaseder, Hannes
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
Finding appropriate and high-quality audio files for the creation of a sound track nowadays presents a serious hurdle to many media producers. As most digital sound archives restrict the categoriza- tion of audio data to verbal taxonomies, this process of retrieving suitable sounds often becomes a tedious and time-consuming part of their work. The research project AllThatSounds tries to en- hance the search procedure by supplying additional, associative and semantic classifications of the audio files. This is achieved by annotating these files with suitable metadata according to a cus- tomized systematic categorization scheme. Moreover, additional data is collected by the evaluation of user profiles and by analyzing the sounds with signal processing methods. Using artificial intel- ligence techniques, similarity distances are calculated between all the audio files in the database, so as to devise a different, highly efficient search algorithm by browsing across similar sounds. The project’s result is a tool for structuring sound databases with an ef- ficient search component, which means to guide users to suitable sounds for their sound track of media
Sponsor
Date
2009-05
Extent
Resource Type
Text
Resource Subtype
Proceedings