Table 2 Comparison results of the first...

Table 2

Comparison results of the first six proposed algorithm in white, babble, f16 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 1a			Figure 1b			Figure 1c
Noise	SNR	ns	s	mean	ns	s	mean	ns	s	mean
white	-10	0.7861	0.9244	0.8959	0.7897	0.9258	0.9023	0.8026	0.9264	0.9058
	-5	0.8296	0.9340	0.9149	0.8121	0.9306	0.9101	0.8319	0.9315	0.9155
	0	0.8455	0.9373	0.9218	0.8251	0.9324	0.9141	0.8465	0.9341	0.9202
	5	0.8535	0.9386	0.9245	0.8366	0.9329	0.9171	0.8530	0.9351	0.9224
	10	0.8590	0.9391	0.9264	0.8431	0.9330	0.9183	0.8575	0.9359	0.9244
babble	-10	0.6874	0.8964	0.8650	0.6754	0.8932	0.8594	0.6637	0.8767	0.8486
	-5	0.7408	0.9137	0.8852	0.7200	0.9089	0.8777	0.7248	0.9046	0.8762
	0	0.7945	0.9264	0.9027	0.7730	0.9227	0.8966	0.7829	0.9220	0.8981
	5	0.8286	0.9329	0.9129	0.8140	0.9309	0.9097	0.8226	0.9301	0.9109
	10	0.8463	0.9361	0.9182	0.8376	0.9340	0.9172	0.8429	0.9337	0.9166
f16	-10	0.5655	0.8180	0.7870	0.6512	0.8834	0.8467	0.5614	0.7960	0.7700
	-5	0.6608	0.8896	0.8543	0.6983	0.9048	0.8693	0.6445	0.8748	0.8362
	0	0.7450	0.9189	0.8876	0.7610	0.9215	0.8900	0.7355	0.9139	0.8772
	5	0.7869	0.9283	0.9026	0.8085	0.9300	0.9050	0.7828	0.9250	0.8951
	10	0.8137	0.9323	0.9119	0.8328	0.9330	0.9151	0.8091	0.9292	0.9076
leopard	-10	0.5288	0.7982	0.7618	0.6778	0.8921	0.8447	0.5987	0.8959	0.8417
	-5	0.5694	0.8369	0.7934	0.7207	0.9067	0.8599	0.7002	0.9152	0.8611
	0	0.6407	0.8788	0.8297	0.7655	0.9187	0.8749	0.7677	0.9242	0.8708
	5	0.7197	0.9123	0.8599	0.8075	0.9282	0.8908	0.8029	0.9286	0.8756
	10	0.7669	0.9258	0.8727	0.8356	0.9332	0.9050	0.8243	0.9315	0.8787
network parameters		233906			439618			233988
Noise scenario		Figure 1d			Figure 1e			Figure 1f
Noise	SNR	ns	s	mean	ns	s	mean	ns	s	mean
white	-10	0.7974	0.9297	0.9076	0.7869	0.9271	0.9035	0.8188	0.9353	0.9140
	-5	0.8315	0.9358	0.9185	0.8240	0.9333	0.9156	0.8419	0.9406	0.9225
	0	0.8485	0.9377	0.9232	0.8439	0.9358	0.9210	0.8527	0.9426	0.9247
	5	0.8570	0.9384	0.9261	0.8542	0.9368	0.9241	0.8595	0.9432	0.9247
	10	0.8625	0.9388	0.9279	0.8594	0.9374	0.9258	0.8649	0.9434	0.9285
babble	-10	0.6639	0.8767	0.8477	0.6747	0.8837	0.8550	0.7033	0.9062	0.8703
	-5	0.7225	0.9049	0.8759	0.7278	0.9064	0.8774	0.7758	0.9246	0.8968
	0	0.7844	0.9234	0.9000	0.7831	0.9229	0.8980	0.8238	0.9346	0.9149
	5	0.8248	0.9319	0.9139	0.8213	0.9314	0.9114	0.8479	0.9397	0.9236
	10	0.8450	0.9355	0.9210	0.8414	0.9348	0.9179	0.8604	0.9419	0.9281
f16	-10	0.5243	0.8161	0.7733	0.6091	0.8305	0.7977	0.6965	0.8951	0.8606
	-5	0.6202	0.8891	0.8464	0.6755	0.8885	0.8464	0.7724	0.9246	0.8942
	0	0.7012	0.9187	0.8834	0.7501	0.9191	0.8784	0.8247	0.9375	0.9135
	5	0.7469	0.9282	0.9014	0.7987	0.9296	0.8970	0.8510	0.9423	0.9210
	10	0.7844	0.9321	0.9095	0.8215	0.9326	0.9104	0.8649	0.9436	0.9244
leopard	-10	0.5636	0.8573	0.8087	0.6362	0.8911	0.8384	0.5622	0.82550	0.7813
	-5	0.5750	0.8796	0.8269	0.6688	0.9096	0.8548	0.6078	0.8657	0.8155
	0	0.6301	0.9030	0.8482	0.7029	0.9201	0.8650	0.6746	0.8988	0.8452
	5	0.7086	0.9209	0.8662	0.7325	0.9253	0.8708	0.7445	0.9196	0.8653
	10	0.7607	0.9296	0.8758	0.7571	0.9278	0.8741	0.7958	0.9304	0.8768
network parameters		233924			234006			388986

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