Benchmark Report¶
Configuration¶
Length of signals: 1024
Repetitions: 100
SNRin values: -5, 0, 10, 20,
Methods¶
contour_filtering
delaunay_triangulation
empty_space
thresholding_garrote
thresholding_hard
sz_classification
pseudo_bayesian_method
sstrd_method
Signals¶
McMultiLinear
McMultiLinear2
McSyntheticMixture
McSyntheticMixture2
McSyntheticMixture3
McSyntheticMixture4
HermiteFunction
Mean results tables:¶
Results shown here are the mean and standard deviation of the performance metric. Best performances are bolded.
Signal: McMultiLinear[View Plot] [Get .csv]¶
Method + Param |
SNRin=-5dB (mean) |
SNRin=-5dB (std) |
SNRin=0dB (mean) |
SNRin=0dB (std) |
SNRin=10dB (mean) |
SNRin=10dB (std) |
SNRin=20dB (mean) |
SNRin=20dB (std) |
|
|---|---|---|---|---|---|---|---|---|---|
0 |
contour_filtering |
-0.51 |
0.22 |
1.60 |
0.40 |
13.09 |
0.19 |
22.32 |
0.18 |
1 |
delaunay_triangulation |
-1.52 |
0.78 |
2.13 |
0.54 |
14.19 |
0.51 |
24.30 |
0.29 |
2 |
empty_space |
-2.01 |
0.77 |
1.97 |
0.67 |
13.94 |
0.56 |
24.05 |
0.30 |
3 |
thresholding_garrote |
0.45 |
0.34 |
5.62 |
0.39 |
15.70 |
0.34 |
25.12 |
0.29 |
4 |
thresholding_hard |
0.11 |
0.08 |
1.18 |
0.27 |
15.67 |
0.39 |
25.64 |
0.33 |
5 |
pseudo_bayesian_method([], [], [], 0.4, 0.4, [], [], [], [], []) |
-2.47 |
0.33 |
3.49 |
0.71 |
13.90 |
1.14 |
24.04 |
1.11 |
6 |
pseudo_bayesian_method([], [], [], 0.4, 0.2, [], [], [], [], []) |
-2.86 |
0.22 |
3.27 |
0.40 |
14.29 |
0.26 |
24.41 |
0.24 |
7 |
sz_classification |
-0.46 |
1.14 |
4.33 |
1.04 |
13.37 |
0.97 |
22.49 |
0.33 |
8 |
sstrd_method |
-3.46 |
0.23 |
1.57 |
0.28 |
11.65 |
0.12 |
21.62 |
0.12 |
Signal: McMultiLinear2[View Plot] [Get .csv]¶
Method + Param |
SNRin=-5dB (mean) |
SNRin=-5dB (std) |
SNRin=0dB (mean) |
SNRin=0dB (std) |
SNRin=10dB (mean) |
SNRin=10dB (std) |
SNRin=20dB (mean) |
SNRin=20dB (std) |
|
|---|---|---|---|---|---|---|---|---|---|
0 |
contour_filtering |
-0.62 |
0.27 |
0.62 |
0.16 |
2.47 |
0.21 |
3.18 |
0.17 |
1 |
delaunay_triangulation |
-1.85 |
0.75 |
1.29 |
0.32 |
7.38 |
0.62 |
6.59 |
0.12 |
2 |
empty_space |
-2.38 |
0.68 |
1.14 |
0.36 |
7.81 |
0.62 |
6.90 |
0.12 |
3 |
thresholding_garrote |
0.14 |
0.30 |
4.79 |
0.29 |
14.89 |
0.26 |
24.37 |
0.25 |
4 |
thresholding_hard |
0.05 |
0.05 |
0.51 |
0.16 |
13.97 |
0.34 |
24.45 |
0.29 |
5 |
pseudo_bayesian_method([], [], [], 0.4, 0.4, [], [], [], [], []) |
-3.29 |
0.21 |
2.32 |
0.54 |
12.75 |
1.82 |
23.50 |
0.91 |
6 |
pseudo_bayesian_method([], [], [], 0.4, 0.2, [], [], [], [], []) |
-3.68 |
0.14 |
1.71 |
0.28 |
13.46 |
0.25 |
23.71 |
0.21 |
7 |
sz_classification |
-0.58 |
0.85 |
3.64 |
0.40 |
13.85 |
0.48 |
23.57 |
1.01 |
8 |
sstrd_method |
-3.77 |
0.28 |
1.60 |
0.45 |
12.96 |
0.24 |
22.99 |
0.20 |
Signal: McSyntheticMixture[View Plot] [Get .csv]¶
Method + Param |
SNRin=-5dB (mean) |
SNRin=-5dB (std) |
SNRin=0dB (mean) |
SNRin=0dB (std) |
SNRin=10dB (mean) |
SNRin=10dB (std) |
SNRin=20dB (mean) |
SNRin=20dB (std) |
|
|---|---|---|---|---|---|---|---|---|---|
0 |
contour_filtering |
-0.14 |
0.27 |
2.10 |
0.59 |
9.22 |
0.92 |
11.27 |
1.24 |
1 |
delaunay_triangulation |
-1.64 |
0.74 |
1.57 |
0.40 |
9.33 |
1.01 |
10.30 |
0.89 |
2 |
empty_space |
-2.12 |
0.74 |
1.63 |
0.51 |
10.10 |
0.73 |
11.37 |
0.66 |
3 |
thresholding_garrote |
0.49 |
0.34 |
5.49 |
0.38 |
15.51 |
0.36 |
24.89 |
0.31 |
4 |
thresholding_hard |
0.16 |
0.11 |
1.59 |
0.33 |
15.48 |
0.35 |
25.14 |
0.32 |
5 |
pseudo_bayesian_method([], [], [], 0.4, 0.4, [], [], [], [], []) |
-1.62 |
0.37 |
3.36 |
0.79 |
12.66 |
1.93 |
19.19 |
1.96 |
6 |
pseudo_bayesian_method([], [], [], 0.4, 0.2, [], [], [], [], []) |
-1.87 |
0.28 |
4.09 |
0.40 |
14.46 |
0.37 |
21.09 |
0.42 |
7 |
sz_classification |
-0.27 |
0.99 |
4.26 |
0.65 |
15.22 |
0.39 |
24.26 |
0.48 |
8 |
sstrd_method |
-2.64 |
0.33 |
2.15 |
0.51 |
10.48 |
2.05 |
20.42 |
3.82 |
Signal: McSyntheticMixture2[View Plot] [Get .csv]¶
Method + Param |
SNRin=-5dB (mean) |
SNRin=-5dB (std) |
SNRin=0dB (mean) |
SNRin=0dB (std) |
SNRin=10dB (mean) |
SNRin=10dB (std) |
SNRin=20dB (mean) |
SNRin=20dB (std) |
|
|---|---|---|---|---|---|---|---|---|---|
0 |
contour_filtering |
1.31 |
0.61 |
6.37 |
0.66 |
15.56 |
0.34 |
24.55 |
0.30 |
1 |
delaunay_triangulation |
-1.11 |
0.85 |
3.29 |
0.77 |
14.83 |
0.54 |
24.60 |
0.56 |
2 |
empty_space |
-1.67 |
0.95 |
3.17 |
1.10 |
14.64 |
0.43 |
24.83 |
0.49 |
3 |
thresholding_garrote |
1.38 |
0.39 |
6.47 |
0.40 |
16.33 |
0.35 |
25.94 |
0.30 |
4 |
thresholding_hard |
1.12 |
0.41 |
6.29 |
0.64 |
16.96 |
0.51 |
26.15 |
0.34 |
5 |
pseudo_bayesian_method([], [], [], 0.4, 0.4, [], [], [], [], []) |
-1.83 |
0.37 |
3.19 |
0.60 |
12.51 |
1.93 |
20.02 |
3.30 |
6 |
pseudo_bayesian_method([], [], [], 0.4, 0.2, [], [], [], [], []) |
-2.30 |
0.19 |
3.14 |
0.22 |
13.58 |
0.27 |
23.10 |
0.53 |
7 |
sz_classification |
0.91 |
1.91 |
5.32 |
1.55 |
14.63 |
0.67 |
23.96 |
0.68 |
8 |
sstrd_method |
-3.15 |
0.23 |
1.95 |
0.25 |
12.15 |
0.32 |
22.78 |
0.59 |
Signal: McSyntheticMixture3[View Plot] [Get .csv]¶
Method + Param |
SNRin=-5dB (mean) |
SNRin=-5dB (std) |
SNRin=0dB (mean) |
SNRin=0dB (std) |
SNRin=10dB (mean) |
SNRin=10dB (std) |
SNRin=20dB (mean) |
SNRin=20dB (std) |
|
|---|---|---|---|---|---|---|---|---|---|
0 |
contour_filtering |
1.60 |
0.72 |
6.93 |
0.97 |
16.99 |
0.48 |
26.15 |
0.51 |
1 |
delaunay_triangulation |
-1.22 |
0.93 |
3.50 |
0.93 |
15.26 |
0.51 |
24.93 |
0.48 |
2 |
empty_space |
-1.80 |
0.89 |
3.34 |
1.23 |
14.89 |
0.51 |
25.17 |
0.53 |
3 |
thresholding_garrote |
1.43 |
0.46 |
6.56 |
0.43 |
16.49 |
0.42 |
26.19 |
0.38 |
4 |
thresholding_hard |
1.24 |
0.44 |
6.59 |
0.72 |
17.80 |
0.46 |
27.00 |
0.40 |
5 |
pseudo_bayesian_method([], [], [], 0.4, 0.4, [], [], [], [], []) |
-1.73 |
0.30 |
3.42 |
0.47 |
12.73 |
0.76 |
18.60 |
2.14 |
6 |
pseudo_bayesian_method([], [], [], 0.4, 0.2, [], [], [], [], []) |
-2.37 |
0.21 |
3.05 |
0.22 |
13.39 |
0.29 |
20.81 |
0.70 |
7 |
sz_classification |
0.92 |
1.83 |
5.34 |
1.84 |
15.06 |
1.04 |
24.60 |
0.57 |
8 |
sstrd_method |
-3.22 |
0.26 |
1.77 |
0.37 |
11.37 |
1.28 |
19.67 |
3.19 |
Signal: McSyntheticMixture4[View Plot] [Get .csv]¶
Method + Param |
SNRin=-5dB (mean) |
SNRin=-5dB (std) |
SNRin=0dB (mean) |
SNRin=0dB (std) |
SNRin=10dB (mean) |
SNRin=10dB (std) |
SNRin=20dB (mean) |
SNRin=20dB (std) |
|
|---|---|---|---|---|---|---|---|---|---|
0 |
contour_filtering |
-0.70 |
0.38 |
3.48 |
0.39 |
13.21 |
0.24 |
22.79 |
0.26 |
1 |
delaunay_triangulation |
-1.36 |
0.80 |
2.40 |
0.47 |
13.59 |
0.48 |
23.57 |
0.26 |
2 |
empty_space |
-1.87 |
0.84 |
2.23 |
0.65 |
13.66 |
0.45 |
23.52 |
0.26 |
3 |
thresholding_garrote |
0.58 |
0.31 |
5.45 |
0.33 |
15.44 |
0.31 |
24.62 |
0.26 |
4 |
thresholding_hard |
0.21 |
0.14 |
1.81 |
0.35 |
13.77 |
0.43 |
24.52 |
0.28 |
5 |
pseudo_bayesian_method([], [], [], 0.4, 0.4, [], [], [], [], []) |
-4.50 |
0.11 |
0.85 |
0.19 |
10.45 |
0.88 |
17.99 |
3.41 |
6 |
pseudo_bayesian_method([], [], [], 0.4, 0.2, [], [], [], [], []) |
-4.81 |
0.07 |
0.33 |
0.07 |
10.66 |
0.09 |
21.05 |
0.16 |
7 |
sz_classification |
-0.30 |
1.22 |
3.82 |
0.82 |
13.25 |
0.57 |
22.82 |
0.37 |
8 |
sstrd_method |
-4.81 |
0.09 |
0.15 |
0.10 |
10.22 |
0.10 |
20.84 |
0.29 |
Signal: HermiteFunction[View Plot] [Get .csv]¶
Method + Param |
SNRin=-5dB (mean) |
SNRin=-5dB (std) |
SNRin=0dB (mean) |
SNRin=0dB (std) |
SNRin=10dB (mean) |
SNRin=10dB (std) |
SNRin=20dB (mean) |
SNRin=20dB (std) |
|
|---|---|---|---|---|---|---|---|---|---|
0 |
contour_filtering |
2.42 |
0.68 |
3.63 |
0.61 |
4.49 |
0.22 |
4.55 |
0.11 |
1 |
delaunay_triangulation |
-0.59 |
1.11 |
5.28 |
1.52 |
19.20 |
1.25 |
30.40 |
1.01 |
2 |
empty_space |
-1.28 |
1.19 |
4.99 |
1.71 |
18.07 |
1.40 |
29.83 |
1.26 |
3 |
thresholding_garrote |
2.03 |
0.53 |
7.09 |
0.53 |
17.15 |
0.52 |
27.17 |
0.52 |
4 |
thresholding_hard |
5.96 |
1.49 |
14.14 |
0.93 |
23.41 |
0.79 |
32.47 |
0.73 |
5 |
pseudo_bayesian_method([], [], [], 0.4, 0.4, [], [], [], [], []) |
0.11 |
1.12 |
1.64 |
1.42 |
3.13 |
1.37 |
3.94 |
0.20 |
6 |
pseudo_bayesian_method([], [], [], 0.4, 0.2, [], [], [], [], []) |
-0.59 |
0.91 |
0.22 |
1.19 |
0.16 |
0.87 |
-0.00 |
0.00 |
7 |
sz_classification |
2.08 |
2.76 |
6.74 |
2.36 |
17.12 |
1.91 |
27.35 |
1.71 |
8 |
sstrd_method |
1.03 |
0.58 |
2.86 |
0.64 |
3.51 |
0.54 |
3.31 |
0.40 |