Combining Features Reduces Hubness in Audio Similarity

In audio based music similarity, a well known effect is the existence of hubs, i.e. songs which appear similar to many other songs without showing any meaningful perceptual similarity. We verify that this effect also exists in very large databases (> 250000 songs) and that it even gets worse with growing size of databases. By combining different aspects of audio similarity we are able to reduce the hub problem while at the same time maintaining a high overall quality of audio similarity.

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