Multimedia Tools and Applications
Melody Recognition with Learned Edit Distances
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Towards a computational model of melody identification in polyphonic music
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On the suitability of combining feature selection and resampling to manage data complexity
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
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In this paper, the problem of identifying the melodic track of a MIDI file in imbalanced scenarios is addressed. A polyphonic MIDI file is a digital score that consists of a set of tracks where usually only one of them contains the melody and the remaining tracks hold the accompaniment. This leads to a two-class imbalance problem that, unlike in previous work, is managed by over-sampling the melody class (the minority one) or by under-sampling the accompaniment class (the majority one) until both classes are the same size. Experimental results over three different music genres prove that learning from balanced training sets clearly provides better results than the standard classification process.