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This paper presents some ﬁndings around musical genres. The main goal is to analyse whether there is any agreement between a group of experts and a community, when deﬁning a set of genres and their relationships. For this purpose, three different experiments are conducted using two datasets: the MP3.com expert taxonomy, and last.fm tags at artist level. The experimental results show a clear agreement for some components of the taxonomy (Blues, HipHop), whilst in other cases (e.g. Rock) there is no correlations. Interestingly enough, the same results are found in the MIREX2007 results for audio genre classiﬁcation task. Thus, showing the fact that a musical genre could have a multi–faceted deﬁnition; using expert based classiﬁcations, dynamic associations derived from the community driven annotations, and content–based analysis would improve genre classiﬁcation, as well as other relevant MIR tasks such as music similarity or music recommendation.