On the use of anti-word models for audio music annotation and retrieval
Journal
IEEE Transactions on Audio, Speech and Language Processing
Journal Volume
17
Journal Issue
8
Pages
1547-1556
Date Issued
2009
Author(s)
Abstract
Query-by-semantic-description (QBSD) is a natural way for searching/annotating music in a large database. To improve QBSD, we propose the use of anti-words for each annotation word based on the concept of supervised multiclass labeling (SML). More specifically, words that are highly associated with the opposite semantic meaning of a word constitute its anti-word set. By modeling both a word and its anti-word set, our annotation system can achieve 31.1% of equal mean per-word precision and recall, while the original SML model achieves 27.8%. Moreover, by constructing the models of the anti-word explicitly, the performance is also significantly improved for the retrieval system, especially when the query keyword is the antonym of an existing annotation word.
SDGs
Type
journal article