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The increase of the quantity of user-generated content experienced in socialmedia has boosted the importance of analysing and organising the content by itsquality. Here, we propose a method that uses audio fingerprinting to organiseand infer the quality of user-generated audio content. The proposed methoddetects the overlapping segments between different audio clips to organise andcluster the data according to events, and to infer the audio quality of thesamples. A test setup with concert recordings manually crawled from YouTube isused to validate the presented method. The results show that the proposedmethod achieves better results than previous methods.

Automatic Organisation and Quality Analysis of User-Generated Content with Audio Fingerprinting