2014
DOI: 10.1007/978-3-319-12093-5_2
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Abstract: Abstract. Much work is focused upon music genre recognition (MGR) from audio recordings, symbolic data, and other modalities. While reviews have been written of some of this work before, no survey has been made of the approaches to evaluating approaches to MGR. This paper compiles a bibliography of work in MGR, and analyzes three aspects of evaluation: experimental designs, datasets, and figures of merit.

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Cited by 68 publications
(94 citation statements)
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References 254 publications
(149 reference statements)
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“…This article rigorously addresses all of these, significantly extending our analysis of GTZAN in several practical ways. Our work not only illuminates results reported by a significant amount of work, but also provides a critical piece to address the non-trivial problems associated with evaluating music machine listening systems in valid ways [1,83,84,[103][104][105].…”
Section: Introductionsupporting
confidence: 53%
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“…This article rigorously addresses all of these, significantly extending our analysis of GTZAN in several practical ways. Our work not only illuminates results reported by a significant amount of work, but also provides a critical piece to address the non-trivial problems associated with evaluating music machine listening systems in valid ways [1,83,84,[103][104][105].…”
Section: Introductionsupporting
confidence: 53%
“…This article is not concerned with other public datasets, which may or may not suffer from the same faults as GTZAN, but which have certainly been used in fewer published works than GTZAN [1]. This article is not concerned with the validity, well-posedness, value, usefulness, or applicability of MGR; or whether MGR is "replaced by," or used in the service of, e.g., music similarity, autotagging, or the like.…”
Section: Delimitationsmentioning
confidence: 99%
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