An Ontological Framework for Retrieving Environmental Sounds Using Semantics and Acoustic Content. (21st October 2010)

• Record Type: Journal Article
• Authors: Wichern, Gordon; Mechtley, Brandon; Fink, Alex; Thornburg, Harvey; Spanias, Andreas
• Other Names: Valle Andrea Academic Editor.
• Is Part Of: EURASIP journal on audio, speech, and music processing. Volume 2010(2010)
• Publication Date: 2010-10-21
• Subjects: Sound -- Recording and reproducing -- Digital techniques -- Periodicals; Computer sound processing -- Periodicals; Computer sound processing; Sound -- Recording and reproducing -- Digital techniques; Periodicals; Electronic journal; Electronic journals; 620.2
• DOI: 10.1155/2010/192363
• Languages: English
• ISSNs: 1687-4714

Abstract

Abstract : Organizing a database of user-contributed environmental sound recordings allows sound files to be linked not only by the semantic tags and labels applied to them, but also to other sounds with similar acoustic characteristics. Of paramount importance in navigating these databases are the problems of retrieving similar sounds using text- or sound-based queries, and automatically annotating unlabeled sounds. We propose an integrated system, which can be used for text-based retrieval of unlabeled audio, content-based query-by-example, and automatic annotation of unlabeled sound files. To this end, we introduce an ontological framework where sounds are connected to each other based on the similarity between acoustic features specifically adapted to environmental sounds, while semantic tags and sounds are connected through link weights that are optimized based on user-provided tags. Furthermore, tags are linked to each other through a measure of semantic similarity, which allows for efficient incorporation of out-of-vocabulary tags, that is, tags that do not yet exist in the database. Results on two freely available databases of environmental sounds contributed and labeled by nonexpert users demonstrate effective recall, precision, and average precision scores for both the text-based retrieval and annotation tasks.