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On the Use of Anti-Word Models for Audio Music Annotation and Retrieval

机译:关于反词模型在音频音乐注释和检索中的使用

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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.
机译:按语义描述查询(QBSD)是在大型数据库中搜索/注释音乐的自然方法。为了改善QBSD,我们建议根据监督多类标签(SML)的概念对每个注释词使用反词。更具体地,与单词的相反语义含义高度相关的单词构成其反单词集。通过对单词及其反单词集进行建模,我们的注释系统可以达到平均37.2%的平均每单词精确度和召回率,而原始SML模型则达到27.8%。此外,通过显式构建反词模型,检索系统的性能也得到了显着提高,尤其是当查询关键词是现有注释词的反义词时。

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