研究文章
飙升排序的联合概率建模的神经高峰火车和波形
|
| 数据集 |
信噪比 |
GMM |
WaveClus方法 |
提出了 |
| 小波 |
主成分分析 |
|
| 数据集1 |
清洁 |
10.64% |
32.02% |
17.98% |
5.60% |
| 15分贝 |
9.73% |
24.5% |
16.79% |
4.77% |
| 10 dB |
9.73% |
31.84% |
21.47% |
4.95% |
| 5分贝 |
11.28% |
31.93% |
32.02% |
6.51% |
| 0分贝 |
10.19% |
32.11% |
31.56% |
9.27% |
| −5分贝 |
20.09% |
32.11% |
32.02% |
15.23% |
| −10 dB |
31.93% |
67.98% |
31.93% |
32.11% |
|
| 数据集2 |
清洁 |
2.09% |
8.49% |
6.56% |
1.86% |
| 15分贝 |
1.96% |
7.56% |
6.20% |
1.76% |
| 10 dB |
1.99% |
7.59% |
6.27% |
1.89% |
| 5分贝 |
2.29% |
19.62% |
11.04% |
2.06% |
| 0分贝 |
3.51% |
19.42% |
14.29% |
3.45% |
| −5分贝 |
6.99% |
19.65% |
19.52% |
5.87% |
| −10 dB |
32.55% |
19.59% |
19.56% |
30.53% |
|
|
