Benchmark Report¶
Configuration¶
Length of signals: 1024
Repetitions: 100
SNRin values: -5, 0, 10, 20,
Methods¶
brevdo_method
contour_filtering
delaunay_triangulation
empty_space
thresholding_garrote
thresholding_hard
pseudo_bayesian_method
sz_classification
Signals¶
McMultiLinear
McMultiLinear2
McSyntheticMixture
McSyntheticMixture2
McSyntheticMixture3
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 |
brevdo_method |
0.49 |
0.05 |
0.76 |
0.03 |
0.97 |
0.00 |
1.00 |
0.00 |
1 |
contour_filtering |
0.30 |
0.05 |
0.60 |
0.06 |
0.98 |
0.00 |
1.00 |
0.00 |
2 |
delaunay_triangulation |
0.43 |
0.08 |
0.74 |
0.07 |
0.98 |
0.00 |
1.00 |
0.00 |
3 |
empty_space |
0.47 |
0.08 |
0.75 |
0.06 |
0.98 |
0.00 |
1.00 |
0.00 |
4 |
thresholding_garrote |
0.58 |
0.03 |
0.86 |
0.01 |
0.99 |
0.00 |
1.00 |
0.00 |
5 |
thresholding_hard |
0.17 |
0.06 |
0.53 |
0.04 |
0.99 |
0.00 |
1.00 |
0.00 |
6 |
pseudo_bayesian_method([], [], [], 0.4, 0.4, [], [], [], [], []) |
0.52 |
0.05 |
0.82 |
0.05 |
0.98 |
0.01 |
1.00 |
0.00 |
7 |
pseudo_bayesian_method([], [], [], 0.4, 0.2, [], [], [], [], []) |
0.52 |
0.03 |
0.82 |
0.02 |
0.98 |
0.00 |
1.00 |
0.00 |
8 |
sz_classification |
0.51 |
0.06 |
0.83 |
0.02 |
0.98 |
0.00 |
1.00 |
0.00 |
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 |
brevdo_method |
0.50 |
0.04 |
0.76 |
0.03 |
0.98 |
0.00 |
1.00 |
0.00 |
1 |
contour_filtering |
0.28 |
0.04 |
0.40 |
0.03 |
0.66 |
0.02 |
0.72 |
0.01 |
2 |
delaunay_triangulation |
0.39 |
0.07 |
0.70 |
0.09 |
0.90 |
0.02 |
0.88 |
0.00 |
3 |
empty_space |
0.44 |
0.06 |
0.71 |
0.07 |
0.91 |
0.01 |
0.89 |
0.00 |
4 |
thresholding_garrote |
0.54 |
0.04 |
0.83 |
0.01 |
0.98 |
0.00 |
1.00 |
0.00 |
5 |
thresholding_hard |
0.11 |
0.07 |
0.38 |
0.05 |
0.98 |
0.00 |
1.00 |
0.00 |
6 |
pseudo_bayesian_method([], [], [], 0.4, 0.4, [], [], [], [], []) |
0.49 |
0.04 |
0.77 |
0.04 |
0.97 |
0.02 |
1.00 |
0.00 |
7 |
pseudo_bayesian_method([], [], [], 0.4, 0.2, [], [], [], [], []) |
0.48 |
0.03 |
0.75 |
0.02 |
0.98 |
0.00 |
1.00 |
0.00 |
8 |
sz_classification |
0.48 |
0.08 |
0.81 |
0.03 |
0.98 |
0.00 |
1.00 |
0.00 |
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 |
brevdo_method |
0.49 |
0.06 |
0.75 |
0.04 |
0.95 |
0.03 |
0.99 |
0.01 |
1 |
contour_filtering |
0.32 |
0.06 |
0.63 |
0.06 |
0.94 |
0.01 |
0.96 |
0.01 |
2 |
delaunay_triangulation |
0.39 |
0.08 |
0.67 |
0.08 |
0.94 |
0.02 |
0.95 |
0.01 |
3 |
empty_space |
0.44 |
0.07 |
0.70 |
0.05 |
0.95 |
0.01 |
0.96 |
0.01 |
4 |
thresholding_garrote |
0.59 |
0.03 |
0.86 |
0.01 |
0.99 |
0.00 |
1.00 |
0.00 |
5 |
thresholding_hard |
0.22 |
0.06 |
0.60 |
0.04 |
0.99 |
0.00 |
1.00 |
0.00 |
6 |
pseudo_bayesian_method([], [], [], 0.4, 0.4, [], [], [], [], []) |
0.53 |
0.06 |
0.79 |
0.05 |
0.97 |
0.02 |
0.99 |
0.00 |
7 |
pseudo_bayesian_method([], [], [], 0.4, 0.2, [], [], [], [], []) |
0.56 |
0.04 |
0.84 |
0.02 |
0.98 |
0.00 |
1.00 |
0.00 |
8 |
sz_classification |
0.54 |
0.08 |
0.83 |
0.03 |
0.99 |
0.00 |
1.00 |
0.00 |
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 |
brevdo_method |
0.55 |
0.04 |
0.78 |
0.02 |
0.97 |
0.00 |
1.00 |
0.00 |
1 |
contour_filtering |
0.60 |
0.08 |
0.89 |
0.02 |
0.99 |
0.00 |
1.00 |
0.00 |
2 |
delaunay_triangulation |
0.50 |
0.09 |
0.79 |
0.03 |
0.98 |
0.00 |
1.00 |
0.00 |
3 |
empty_space |
0.53 |
0.07 |
0.80 |
0.03 |
0.98 |
0.00 |
1.00 |
0.00 |
4 |
thresholding_garrote |
0.70 |
0.03 |
0.89 |
0.01 |
0.99 |
0.00 |
1.00 |
0.00 |
5 |
thresholding_hard |
0.51 |
0.07 |
0.89 |
0.02 |
0.99 |
0.00 |
1.00 |
0.00 |
6 |
pseudo_bayesian_method([], [], [], 0.4, 0.4, [], [], [], [], []) |
0.59 |
0.06 |
0.81 |
0.04 |
0.97 |
0.02 |
0.99 |
0.01 |
7 |
pseudo_bayesian_method([], [], [], 0.4, 0.2, [], [], [], [], []) |
0.60 |
0.03 |
0.82 |
0.01 |
0.98 |
0.00 |
1.00 |
0.00 |
8 |
sz_classification |
0.69 |
0.06 |
0.87 |
0.03 |
0.98 |
0.00 |
1.00 |
0.00 |
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 |
brevdo_method |
0.52 |
0.05 |
0.75 |
0.03 |
0.96 |
0.02 |
0.99 |
0.01 |
1 |
contour_filtering |
0.63 |
0.08 |
0.90 |
0.03 |
0.99 |
0.00 |
1.00 |
0.00 |
2 |
delaunay_triangulation |
0.50 |
0.08 |
0.79 |
0.03 |
0.98 |
0.00 |
1.00 |
0.00 |
3 |
empty_space |
0.52 |
0.08 |
0.79 |
0.04 |
0.98 |
0.00 |
1.00 |
0.00 |
4 |
thresholding_garrote |
0.70 |
0.03 |
0.90 |
0.01 |
0.99 |
0.00 |
1.00 |
0.00 |
5 |
thresholding_hard |
0.53 |
0.07 |
0.89 |
0.02 |
0.99 |
0.00 |
1.00 |
0.00 |
6 |
pseudo_bayesian_method([], [], [], 0.4, 0.4, [], [], [], [], []) |
0.59 |
0.05 |
0.81 |
0.03 |
0.97 |
0.01 |
0.99 |
0.00 |
7 |
pseudo_bayesian_method([], [], [], 0.4, 0.2, [], [], [], [], []) |
0.58 |
0.04 |
0.81 |
0.01 |
0.98 |
0.00 |
1.00 |
0.00 |
8 |
sz_classification |
0.69 |
0.05 |
0.87 |
0.04 |
0.98 |
0.00 |
1.00 |
0.00 |
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 |
brevdo_method |
0.56 |
0.11 |
0.70 |
0.06 |
0.74 |
0.04 |
0.73 |
0.03 |
1 |
contour_filtering |
0.65 |
0.07 |
0.75 |
0.04 |
0.80 |
0.01 |
0.81 |
0.01 |
2 |
delaunay_triangulation |
0.57 |
0.10 |
0.85 |
0.05 |
0.99 |
0.00 |
1.00 |
0.00 |
3 |
empty_space |
0.57 |
0.09 |
0.84 |
0.04 |
0.99 |
0.00 |
1.00 |
0.00 |
4 |
thresholding_garrote |
0.76 |
0.03 |
0.91 |
0.01 |
0.99 |
0.00 |
1.00 |
0.00 |
5 |
thresholding_hard |
0.88 |
0.05 |
0.98 |
0.00 |
1.00 |
0.00 |
1.00 |
0.00 |
6 |
pseudo_bayesian_method([], [], [], 0.4, 0.4, [], [], [], [], []) |
0.33 |
0.27 |
0.46 |
0.30 |
0.65 |
0.25 |
0.77 |
0.01 |
7 |
pseudo_bayesian_method([], [], [], 0.4, 0.2, [], [], [], [], []) |
0.15 |
0.25 |
0.14 |
0.27 |
0.04 |
0.17 |
0.00 |
0.00 |
8 |
sz_classification |
0.77 |
0.09 |
0.91 |
0.04 |
0.99 |
0.00 |
1.00 |
0.00 |