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OFAI-TR-2016-09 ( 198kB PDF file)

Hubness aware outlier detection for music genre recognition

Arthur Flexer

Outlier detection is the task of automatic identification of unknown data not covered by training data (e.g. a new genre in genre recognition). We explore outlier detection in the presence of hubs and anti-hubs, i.e. data objects which appear to be either very close or very far from most other data due to a problem of measuring distances in high dimensions. We compare a classic distance based method to two new approaches, which have been designed to counter the negative effects of hubness, on two standard music genre data sets. We demonstrate that anti-hubs are responsible for many detection errors and that this can be improved by using a hubness-aware approach.

Keywords: music information retrieval, outlier detection, hubness, curse of dimensionality, genre recognition

Citation: Flexer A.: Hubness aware outlier detection for music genre recognition, in Proceedings of the 19th International Conference on Digital Audio Effects (DAFx-16), pp. 69-75, 2016.