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2011
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Automatic audio tag classification via semi-supervised canonical density estimation

7 years 11 months ago
Automatic audio tag classification via semi-supervised canonical density estimation
We propose a novel semi-supervised method for building a statistical model that represents the relationship between sounds and text labels (“tags”). The proposed method, named semi-supervised canonical density estimation, makes use of unlabeled sound data in two ways: 1) a low-dimensional latent space representing topics of sounds is extracted by a semi-supervised variant of canonical correlation analysis, and 2) topic models are learned by multi-class extension of semi-supervised kernel density estimation in the topic space. Real-world audio tagging experiments indicate that our proposed method improves the accuracy even when only a small number of labeled sounds are available.
Jun Takagi, Yasunori Ohishi, Akisato Kimura, Masas
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Jun Takagi, Yasunori Ohishi, Akisato Kimura, Masashi Sugiyama, Makoto Yamada, Hirokazu Kameoka
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