Table 10.

Results of deep learning classifiers using hybrid features.

MFCC
Model	Class	Precision	Recall	F1 Score	Model	Class	Precision	Recall	F1 Score
LSTM	0	0.33	1.00	0.50	CNN	0	0.96	0.86	0.81
	1	0.00	0.00	0.00		1	94.0	1.00	97.0
	2	0.00	0.00	0.00		2	0.89	0.92	0.91
	Micro avg.	0.11	0.33	0.17		Micro avg.	0.93	0.93	0.93
	Weighted avg.	0.11	0.33	0.16		Weighted avg.	0.93	0.93	0.93
	Accuracy	0.33				Accuracy	0.93
RNN	0	0.57	0.78	0.66	GRU	0	0.82	0.90	0.86
	1	0.96	1.00	0.98		1	0.96	1.00	0.98
	2	0.69	0.42	0.52		2	0.89	0.77	0.83
	Micro avg.	0.74	0.73	0.72		Micro avg.	0.89	0.89	0.89
	Weighted avg.	0.73	0.72	0.71		Weighted avg.	0.89	0.89	0.89
	Accuracy	0.72				Accuracy	0.89

CQT
Model	Class	Precision	Recall	F1 Score	Model	Class	Precision	Recall	F1 Score
LSTM	0	0.36	1.00	0.53	CNN	0	0.90	0.83	0.86
	1	0.00	0.00	0.00		1	94.0	1.00	97.0
	2	0.00	0.00	0.00		2	0.84	0.87	0.86
	Micro avg.	0.12	0.33	0.18		Micro avg.	0.89	0.90	0.90
	Weighted avg.	0.16	0.36	0.19		Weighted avg.	0.89	0.89	0.89
	Accuracy	0.36				Accuracy	0.89
RNN	0	0.53	0.46	0.49	GRU	0	0.83	0.88	0.85
	1	0.87	0.91	0.89		1	0.98	1.00	0.99
	2	0.51	0.57	0.54		2	0.86	0.79	0.82
	Micro avg.	0.64	0.64	0.64		Micro avg.	0.89	0.89	0.89
	Weighted avg.	0.62	0.63	0.63		Weighted avg.	0.88	0.88	0.88
	Accuracy	0.63				Accuracy	0.88