Table 5.

Classification performance per feature per classifier (original Vgg16 with data augmentation) after 100 training epochs.

Feature	Original Vgg16 with data augmentation
	ACC	Prec	Recall	F1	kappa	AUC
SPEC	0.63	0.61	0.70	0.65	0.25	0.63
Chroma	0.56	0.58	0.44	0.50	0.13	0.56
MFCC	0.61	0.60	0.63	0.62	0.21	0.60
MelSpectrum	0.62	0.63	0.56	0.60	0.23	0.61
PowerSPEC	0.60	0.63	0.46	0.53	0.20	0.60
RAW	0.57	0.63	0.37	0.46	0.15	0.57
Tonal	0.54	0.60	0.22	0.32	0.07	0.54
All features	0.53	0.52	0.88	0.65	0.05	0.53