Table 14.

K-fold cross-validation results with MFCC, CQT features, and CTGAN augmentation using machine learning models.

Features	DT	SVC	KNN	LR	NB	RF	Voting (Hard)	Voting (Soft)
MFCC	0.93(± 0.02)	1.00 (± 0.00)	1.00 (± 0.00)	1.00 (± 0.00)	1.00 (± 0.00)	1.00 (± 0.00)	-	-
CQT	0.91(± 0.02)	0.99 (± 0.01)	0.78 (± 0.05)	0.99 (± 0.01)	1.00 (± 0.00)	1.00 (± 0.00)	0.99 (± 0.01)	1.00 (± 0.00)
MFCC+CQT	0.83(± 0.04)	1.00 (± 0.00)	0.52 (± 0.04)	1.00 (± 0.00)	1.00 (± 0.00)	1.00 (± 0.00)	1.00 (± 0.00)	1.00 (± 0.00)