Revisiting Inter-Genre Similarity

We revisit the idea of “inter-genre similarity” (IGS) for machine learning in general, and music genre recognition in particular. We show analytically that the probability of error for IGS is higher than naive Bayes classification with zero-one loss (NB). We show empirically that IGS does not perform well, even for data that satisfies all its assumptions.