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OFAI-TR-2013-01 ( 169kB PDF file)

Using mutual proximity for novelty detection in audio music similarity

Arthur Flexer, Dominik Schnitzer

Mutual proximity rescales distance spaces to avoid negative e ffects of the curse of dimensionality. It results in probabilistic estimates of the proximity of data objects. We use these probabilities directly for novelty detection, i.e. the automatic identi cation of unknown data not covered by training data (e.g. a new genre in genre classi fication). Comparing this new approach with a distance based detection method we demonstrate improved performance on a standard music data set.

Keywords: music information retrieval, outlier detection, hubness, curse of dimensioanlity

Citation: Flexer A., Schnitzer D.: Using mutual proximity for novelty detection in audio music similarity, in Procceedings of the 6th International Workshop on Machine Learning and Music, Prague, Czech Republic, 2013.