The Importance of Cross Database Evaluation in Sound Classification
Résumé
In numerous articles (Martin and Kim, 1998; Fraser and Fujinaga, 1999; and many others) sound classification algorithms are evaluated using "self classification" - the learning and test groups are randomly selected out of the same sound database. We will show that "self classification" is not necessarily a good statistic for the ability of a classification algorithm to learn, generalize or classify well. We introduce the alternative "Minus-1 DB" evaluation method and demonstrate that it does not have the shortcomings of "self classification".
Domaines
Acoustique [physics.class-ph] Traitement du signal et de l'image [eess.SP] Son [cs.SD] Neurosciences Acoustique [physics.class-ph] Vibrations [physics.class-ph] Vibrations [physics.class-ph] Mécanique des solides [physics.class-ph] Mécanique des structures [physics.class-ph] Musique, musicologie et arts de la scène
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