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The GTZAN dataset appears in at least 100 published works, and is themost-used public dataset for evaluation in machine listening research for musicgenre recognition (MGR). Our recent work, however, shows GTZAN has severalfaults (repetitions, mislabelings, and distortions), which challenge theinterpretability of any result derived using it. In this article, we disprovethe claims that all MGR systems are affected in the same ways by these faults,and that the performances of MGR systems in GTZAN are still meaningfullycomparable since they all face the same faults. We identify and analyze thecontents of GTZAN, and provide a catalog of its faults. We review how GTZAN hasbeen used in MGR research, and find few indications that its faults have beenknown and considered. Finally, we rigorously study the effects of its faults onevaluating five different MGR systems. The lesson is not to banish GTZAN, butto use it with consideration of its contents.

The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use