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New Automatic Taxonomy Generation Algorithm for the Audio Genre Classification  

Choi, Tack-Sung (연세대학교 전기전자공학과)
Moon, Sun-Kook (연세대학교 전기전자공학과)
Park, Young-Cheol (연세대학교 컴퓨터정보통신공학부)
Youn, Dae-Hee (연세대학교 전기전자공학과)
Lee, Seok-Pil (전자부품연구원(KETI) 디지털미디어 연구센터)
Publication Information
The Journal of the Acoustical Society of Korea / v.27, no.3, 2008 , pp. 111-118 More about this Journal
Abstract
In this paper, we propose a new automatic taxonomy generation algorithm for the audio genre classification. The proposed algorithm automatically generates hierarchical taxonomy based on the estimated classification accuracy at all possible nodes. The estimation of classification accuracy in the proposed algorithm is conducted by applying the training data to classifier using k-fold cross validation. Subsequent classification accuracy is then to be tested at every node which consists of two clusters by applying one-versus-one support vector machine. In order to assess the performance of the proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigated classification performance using the proposed algorithm and previous flat classifiers. The classification accuracy reaches to 89 percent with proposed scheme, which is 5 to 25 percent higher than the previous flat classification methods. Using low-dimensional feature vectors, in particular, it is 10 to 25 percent higher than previous algorithms for classification experiments.
Keywords
Feature selection algorithm; Genre classification; Hierarchy; Taxonomy; Wrapper algorithm;
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