Automatic Music Tag Classification Based On Block-Level Features

In this paper we propose to use a set of block-level audio features for automatic tag prediction. As the proposed feature set is extremely high-dimensional we will investigate the Principal Component Analysis (PCA) as compression method to make the tag classification computationally tractable. We will then compare this block-level feature set to a standard feature set that is used in a state-of-theart tag prediction approach. To compare the two feature sets we report on the tag classification results obtained for two publicly available tag classification datasets using the same classification approach for both feature sets. We will show that the proposed features set outperform the standard feature set, thus contributing to the state-of-the-art in automatic tag prediction.

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