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Centroid-model based music similarity with alpha divergence
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 Title & Authors
Centroid-model based music similarity with alpha divergence
Seo, Jin Soo; Kim, Jeonghyun; Park, Jihyun;
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Music-similarity computation is crucial in developing music information retrieval systems for browsing and classification. This paper overviews the recently-proposed centroid-model based music retrieval method and applies the distributional similarity measures to the model for retrieval-performance evaluation. Probabilistic distance measures (also called divergence) compute the distance between two probability distributions in a certain sense. In this paper, we consider the alpha divergence in computing distance between two centroid models for music retrieval. The alpha divergence includes the widely-used Kullback-Leibler divergence and Bhattacharyya distance depending on the values of alpha. Experiments were conducted on both genre and singer datasets. We compare the music-retrieval performance of the distributional similarity with that of the vector distances. The experimental results show that the alpha divergence improves the performance of the centroid-model based music retrieval.
Music retrieval;Music similarity;Alpha divergence;Renyi divergence;KL divergence;Bhattacharyya distance;
 Cited by
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