# 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]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/plot_McMultiLinear.html) [[Get .csv]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/results_McMultiLinear.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]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/plot_McMultiLinear2.html) [[Get .csv]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/results_McMultiLinear2.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]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/plot_McSyntheticMixture.html) [[Get .csv]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/results_McSyntheticMixture.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]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/plot_McSyntheticMixture2.html) [[Get .csv]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/results_McSyntheticMixture2.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]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/plot_McSyntheticMixture3.html) [[Get .csv]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/results_McSyntheticMixture3.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]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/plot_McSyntheticMixture4.html) [[Get .csv]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/results_McSyntheticMixture4.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]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/plot_HermiteFunction.html) [[Get .csv]](https://jmiramont.github.io/benchmarks-detection-denoising/results/denoising/results_HermiteFunction.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 |