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Availability of large amounts of raw unlabeled data has sparked the recent surge in semi-supervised learning research. In most works, however, it is assumed that labeled and unlabeled data come from the same distribution. This restriction is removed in the self-taught learning approach where unlabeled data can be different, but nevertheless have similar structure. First, a representation is learned from the unlabeled data via sparse coding and then it is applied to the labeled data used for classification. In this work, we implemented this method for the music genre classification task using two different databases: one as unlabeled data pool and the other for supervised classifier training. Music pieces come from 10 and 6 genres for each database respectively, while only one genre is common for both of them. Results from wide variety of experimental settings show that the self-taught learning method improves the classification rate when the amount of labeled data is small and, more interestingly, that consistent improvement can be achieved for a wide range of unlabeled data sizes.