Table 3 Comparison results of the last...

Table 3

Comparison results of the last two proposed algorithm in white, babble, fl6 and leopard noises with different SNRs. We report the Average Precision (AP) for each class, and the mean Average Precision (mAP) over all the classes.

Noise scenario		Figure 2a			Figure 2b
Noise	SNR	ns	s	mean	ns	s	mean
white	-10	0.8623	0.9381	0.9310	0.8696	0.9405	0.9313
	-5	0.8792	0.9422	0.9380	0.8841	0.9450	0.9397
	0	0.8865	0.9438	0.9414	0.8904	0.9469	0.9437
	5	0.8892	0.9441	0.9428	0.8927	0.9474	0.9454
	10	0.8896	0.9436	0.9432	0.8932	0.9473	0.9461
babble	-10	0.7957	0.9148	0.9056	0.7953	0.9163	0.9034
	-5	0.8199	0.9254	0.9159	0.8268	0.9299	0.9180
	0	0.8467	0.9347	0.9263	0.8563	0.9395	0.9300
	5	0.8682	0.9403	0.9339	0.8754	0.9444	0.9373
	10	0.8811	0.9429	0.9387	0.8849	0.9465	0.9411
f16	-10	0.7792	0.9099	0.8919	0.8112	0.9254	0.9120
	-5	0.8022	0.9234	0.9061	0.8418	0.9361	0.9232
	0	0.8383	0.9350	0.9208	0.8655	0.9422	0.9311
	5	0.8673	0.9406	0.9310	0.8789	0.9452	0.9363
	10	0.8810	0.9425	0.9371	0.8864	0.9467	0.9403
leopard	-10	0.6922	0.8862	0.8703	0.7548	0.9100	0.8720
	-5	0.7100	0.8966	0.8779	0.7693	0.9187	0.8786
	0	0.7404	0.9102	0.8887	0.7871	0.9267	0.8849
	5	0.7806	0.9230	0.8989	0.8072	0.9332	0.8906
	10	0.8197	0.9318	0.9056	0.8259	0.9375	0.8958
network parameters		363186			647108

Noise scenario		Figure 2a			Figure 2b
Noise	SNR	ns	s	mean	ns	s	mean
white	-10	0.8623	0.9381	0.9310	0.8696	0.9405	0.9313
	-5	0.8792	0.9422	0.9380	0.8841	0.9450	0.9397
	0	0.8865	0.9438	0.9414	0.8904	0.9469	0.9437
	5	0.8892	0.9441	0.9428	0.8927	0.9474	0.9454
	10	0.8896	0.9436	0.9432	0.8932	0.9473	0.9461
babble	-10	0.7957	0.9148	0.9056	0.7953	0.9163	0.9034
	-5	0.8199	0.9254	0.9159	0.8268	0.9299	0.9180
	0	0.8467	0.9347	0.9263	0.8563	0.9395	0.9300
	5	0.8682	0.9403	0.9339	0.8754	0.9444	0.9373
	10	0.8811	0.9429	0.9387	0.8849	0.9465	0.9411
f16	-10	0.7792	0.9099	0.8919	0.8112	0.9254	0.9120
	-5	0.8022	0.9234	0.9061	0.8418	0.9361	0.9232
	0	0.8383	0.9350	0.9208	0.8655	0.9422	0.9311
	5	0.8673	0.9406	0.9310	0.8789	0.9452	0.9363
	10	0.8810	0.9425	0.9371	0.8864	0.9467	0.9403
leopard	-10	0.6922	0.8862	0.8703	0.7548	0.9100	0.8720
	-5	0.7100	0.8966	0.8779	0.7693	0.9187	0.8786
	0	0.7404	0.9102	0.8887	0.7871	0.9267	0.8849
	5	0.7806	0.9230	0.8989	0.8072	0.9332	0.8906
	10	0.8197	0.9318	0.9056	0.8259	0.9375	0.8958
network parameters		363186			647108

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