Benchmark Report [Results .csv]

Configuration

Length of signals: 8192

Repetitions: 100

SNRin values: -5, 0, 5, 10, 15, 20,

Methods

• dt

• ht

Signals

• 6_male

• 6_female

• cello

• trumpet

Mean results tables:

The results shown here are the average and 95% CI of the performance metric with Bonferroni correction. Best performances are bolded.

Signal: 6_male [View Plot] [Get .csv]

	Method + Param	SNRin=-5dB (average)	SNRin=-5dB (CI)	SNRin=0dB (average)	SNRin=0dB (CI)	SNRin=5dB (average)	SNRin=5dB (CI)	SNRin=10dB (average)	SNRin=10dB (CI)	SNRin=15dB (average)	SNRin=15dB (CI)	SNRin=20dB (average)	SNRin=20dB (CI)
0	dt{‘scale_fun’: <function scale_fun_APF at 0x7accaf7ed870>}	1.18	[‘1.16’, ‘1.19’]	1.29	[‘1.28’, ‘1.31’]	1.47	[‘1.45’, ‘1.48’]	1.71	[‘1.69’, ‘1.73’]	2.00	[‘1.97’, ‘2.02’]	2.31	[‘2.28’, ‘2.33’]
1	dt{‘scale_fun’: <function scale_fun_Fvs at 0x7accb0468d30>}	1.17	[‘1.15’, ‘1.19’]	1.29	[‘1.27’, ‘1.32’]	1.43	[‘1.40’, ‘1.47’]	1.61	[‘1.55’, ‘1.67’]	1.82	[‘1.72’, ‘1.91’]	2.04	[‘1.93’, ‘2.16’]
2	dt{‘scale_fun’: <function scale_fun_F at 0x7accaf7ed7e0>}	1.19	[‘1.18’, ‘1.21’]	1.33	[‘1.31’, ‘1.34’]	1.50	[‘1.49’, ‘1.52’]	1.77	[‘1.75’, ‘1.79’]	2.10	[‘2.07’, ‘2.12’]	2.44	[‘2.41’, ‘2.46’]
3	dt{‘LB’: 1.0}	1.22	[‘1.20’, ‘1.23’]	1.34	[‘1.33’, ‘1.35’]	1.50	[‘1.49’, ‘1.52’]	1.75	[‘1.73’, ‘1.76’]	2.08	[‘2.06’, ‘2.11’]	2.49	[‘2.46’, ‘2.51’]
4	dt{‘LB’: 1.25}	1.22	[‘1.21’, ‘1.24’]	1.35	[‘1.34’, ‘1.36’]	1.51	[‘1.50’, ‘1.53’]	1.76	[‘1.74’, ‘1.78’]	2.10	[‘2.08’, ‘2.12’]	2.50	[‘2.47’, ‘2.52’]
5	dt{‘LB’: 1.5}	1.19	[‘1.17’, ‘1.20’]	1.31	[‘1.30’, ‘1.33’]	1.49	[‘1.48’, ‘1.51’]	1.76	[‘1.74’, ‘1.77’]	2.08	[‘2.06’, ‘2.10’]	2.39	[‘2.37’, ‘2.42’]
6	dt{‘LB’: 1.75}	1.06	[‘1.05’, ‘1.06’]	1.10	[‘1.09’, ‘1.11’]	1.20	[‘1.19’, ‘1.21’]	1.33	[‘1.31’, ‘1.35’]	1.47	[‘1.45’, ‘1.49’]	1.62	[‘1.59’, ‘1.64’]
7	dt{‘LB’: 2.0}	1.04	[‘1.03’, ‘1.06’]	1.05	[‘1.04’, ‘1.07’]	1.07	[‘1.07’, ‘1.08’]	1.11	[‘1.10’, ‘1.11’]	1.16	[‘1.15’, ‘1.17’]	1.20	[‘1.19’, ‘1.20’]
8	dt{‘LB’: 2.25}	1.06	[‘1.05’, ‘1.08’]	1.05	[‘1.05’, ‘1.06’]	1.12	[‘1.07’, ‘1.19’]	1.18	[‘1.11’, ‘1.31’]	1.25	[‘1.15’, ‘1.40’]	1.14	[‘1.12’, ‘1.17’]
9	dt{‘LB’: 2.5}	1.05	[‘1.05’, ‘1.06’]	1.05	[‘1.05’, ‘1.06’]	1.05	[‘1.05’, ‘1.06’]	1.06	[‘1.05’, ‘1.07’]	1.12	[‘1.08’, ‘1.24’]	2.16	[‘1.78’, ‘2.56’]
10	dt{‘LB’: 2.75}	1.05	[‘1.05’, ‘1.06’]	1.05	[‘1.05’, ‘1.06’]	1.05	[‘1.05’, ‘1.06’]	1.05	[‘1.05’, ‘1.06’]	1.05	[‘1.05’, ‘1.06’]	1.06	[‘1.05’, ‘1.07’]
11	dt{‘LB’: 3.0}	1.05	[‘1.05’, ‘1.06’]	1.05	[‘1.05’, ‘1.06’]	1.05	[‘1.05’, ‘1.06’]	1.05	[‘1.05’, ‘1.06’]	1.05	[‘1.05’, ‘1.06’]	1.05	[‘1.05’, ‘1.06’]
12	ht{‘coeff’: 0.5, ‘Nfft’: 4096}	1.23	[‘1.21’, ‘1.24’]	1.35	[‘1.34’, ‘1.36’]	1.51	[‘1.50’, ‘1.53’]	1.77	[‘1.75’, ‘1.79’]	2.12	[‘2.10’, ‘2.14’]	2.53	[‘2.50’, ‘2.55’]
13	ht{‘coeff’: 1.0, ‘Nfft’: 4096}	1.24	[‘1.22’, ‘1.25’]	1.37	[‘1.36’, ‘1.38’]	1.53	[‘1.52’, ‘1.55’]	1.79	[‘1.77’, ‘1.81’]	2.15	[‘2.13’, ‘2.17’]	2.58	[‘2.55’, ‘2.61’]
14	ht{‘coeff’: 1.5, ‘Nfft’: 4096}	1.23	[‘1.21’, ‘1.25’]	1.37	[‘1.35’, ‘1.38’]	1.53	[‘1.51’, ‘1.54’]	1.77	[‘1.75’, ‘1.79’]	2.09	[‘2.07’, ‘2.11’]	2.51	[‘2.49’, ‘2.54’]
15	ht{‘coeff’: 2.0, ‘Nfft’: 4096}	1.23	[‘1.21’, ‘1.25’]	1.36	[‘1.35’, ‘1.37’]	1.51	[‘1.49’, ‘1.52’]	1.72	[‘1.70’, ‘1.74’]	1.99	[‘1.97’, ‘2.01’]	2.41	[‘2.38’, ‘2.44’]
16	ht{‘coeff’: 2.5, ‘Nfft’: 4096}	1.23	[‘1.21’, ‘1.25’]	1.35	[‘1.34’, ‘1.37’]	1.50	[‘1.48’, ‘1.51’]	1.67	[‘1.66’, ‘1.69’]	1.91	[‘1.90’, ‘1.93’]	2.32	[‘2.29’, ‘2.35’]
17	ht{‘coeff’: 3.0, ‘Nfft’: 4096}	1.23	[‘1.21’, ‘1.25’]	1.36	[‘1.35’, ‘1.37’]	1.49	[‘1.48’, ‘1.51’]	1.64	[‘1.63’, ‘1.66’]	1.87	[‘1.86’, ‘1.89’]	2.28	[‘2.25’, ‘2.31’]
18	ht{‘coeff’: 3.5, ‘Nfft’: 4096}	1.23	[‘1.21’, ‘1.25’]	1.37	[‘1.35’, ‘1.38’]	1.48	[‘1.47’, ‘1.50’]	1.63	[‘1.62’, ‘1.65’]	1.85	[‘1.83’, ‘1.86’]	2.26	[‘2.23’, ‘2.30’]
19	ht{‘coeff’: 4.0, ‘Nfft’: 4096}	1.23	[‘1.21’, ‘1.25’]	1.38	[‘1.36’, ‘1.39’]	1.48	[‘1.47’, ‘1.50’]	1.64	[‘1.62’, ‘1.65’]	1.83	[‘1.82’, ‘1.85’]	2.26	[‘2.23’, ‘2.30’]
20	ht{‘coeff’: 4.5, ‘Nfft’: 4096}	1.22	[‘1.20’, ‘1.25’]	1.39	[‘1.37’, ‘1.41’]	1.49	[‘1.47’, ‘1.51’]	1.65	[‘1.63’, ‘1.66’]	1.84	[‘1.83’, ‘1.86’]	2.26	[‘2.23’, ‘2.30’]
21	ht{‘coeff’: 5.0, ‘Nfft’: 4096}	1.23	[‘1.21’, ‘1.25’]	1.40	[‘1.38’, ‘1.42’]	1.51	[‘1.49’, ‘1.53’]	1.65	[‘1.64’, ‘1.67’]	1.85	[‘1.84’, ‘1.87’]	2.25	[‘2.22’, ‘2.29’]

Signal: 6_female [View Plot] [Get .csv]

	Method + Param	SNRin=-5dB (average)	SNRin=-5dB (CI)	SNRin=0dB (average)	SNRin=0dB (CI)	SNRin=5dB (average)	SNRin=5dB (CI)	SNRin=10dB (average)	SNRin=10dB (CI)	SNRin=15dB (average)	SNRin=15dB (CI)	SNRin=20dB (average)	SNRin=20dB (CI)
0	dt{‘scale_fun’: <function scale_fun_APF at 0x7accaf7ed870>}	1.16	[‘1.15’, ‘1.17’]	1.30	[‘1.29’, ‘1.32’]	1.56	[‘1.54’, ‘1.58’]	1.88	[‘1.86’, ‘1.90’]	2.25	[‘2.23’, ‘2.27’]	2.59	[‘2.57’, ‘2.61’]
1	dt{‘scale_fun’: <function scale_fun_Fvs at 0x7accb0468d30>}	1.16	[‘1.14’, ‘1.17’]	1.31	[‘1.29’, ‘1.32’]	1.54	[‘1.52’, ‘1.56’]	1.84	[‘1.82’, ‘1.86’]	2.20	[‘2.17’, ‘2.22’]	2.54	[‘2.51’, ‘2.57’]
2	dt{‘scale_fun’: <function scale_fun_F at 0x7accaf7ed7e0>}	1.17	[‘1.16’, ‘1.18’]	1.31	[‘1.30’, ‘1.33’]	1.54	[‘1.52’, ‘1.56’]	1.85	[‘1.83’, ‘1.87’]	2.20	[‘2.18’, ‘2.23’]	2.59	[‘2.57’, ‘2.61’]
3	dt{‘LB’: 1.0}	1.17	[‘1.16’, ‘1.18’]	1.27	[‘1.26’, ‘1.28’]	1.46	[‘1.44’, ‘1.47’]	1.74	[‘1.72’, ‘1.76’]	2.09	[‘2.06’, ‘2.11’]	2.49	[‘2.47’, ‘2.52’]
4	dt{‘LB’: 1.25}	1.18	[‘1.17’, ‘1.18’]	1.29	[‘1.28’, ‘1.29’]	1.48	[‘1.47’, ‘1.50’]	1.77	[‘1.75’, ‘1.79’]	2.12	[‘2.10’, ‘2.14’]	2.53	[‘2.50’, ‘2.55’]
5	dt{‘LB’: 1.5}	1.17	[‘1.16’, ‘1.18’]	1.31	[‘1.30’, ‘1.33’]	1.55	[‘1.53’, ‘1.57’]	1.86	[‘1.84’, ‘1.88’]	2.22	[‘2.20’, ‘2.24’]	2.60	[‘2.57’, ‘2.62’]
6	dt{‘LB’: 1.75}	1.06	[‘1.05’, ‘1.07’]	1.16	[‘1.14’, ‘1.17’]	1.40	[‘1.38’, ‘1.43’]	1.78	[‘1.75’, ‘1.80’]	2.11	[‘2.09’, ‘2.14’]	2.41	[‘2.38’, ‘2.43’]
7	dt{‘LB’: 2.0}	1.03	[‘1.02’, ‘1.03’]	1.04	[‘1.04’, ‘1.05’]	1.10	[‘1.09’, ‘1.11’]	1.27	[‘1.25’, ‘1.29’]	1.55	[‘1.53’, ‘1.58’]	1.79	[‘1.77’, ‘1.81’]
8	dt{‘LB’: 2.25}	1.05	[‘1.04’, ‘1.06’]	1.05	[‘1.04’, ‘1.05’]	1.07	[‘1.05’, ‘1.09’]	1.07	[‘1.06’, ‘1.08’]	1.12	[‘1.11’, ‘1.14’]	1.23	[‘1.21’, ‘1.25’]
9	dt{‘LB’: 2.5}	1.05	[‘1.05’, ‘1.05’]	1.05	[‘1.05’, ‘1.05’]	1.05	[‘1.05’, ‘1.06’]	1.07	[‘1.05’, ‘1.09’]	1.11	[‘1.08’, ‘1.15’]	1.12	[‘1.09’, ‘1.18’]
10	dt{‘LB’: 2.75}	1.05	[‘1.05’, ‘1.05’]	1.05	[‘1.05’, ‘1.05’]	1.05	[‘1.05’, ‘1.05’]	1.05	[‘1.05’, ‘1.05’]	1.05	[‘1.05’, ‘1.05’]	1.05	[‘1.05’, ‘1.05’]
11	dt{‘LB’: 3.0}	1.05	[‘1.05’, ‘1.05’]	1.05	[‘1.05’, ‘1.05’]	1.05	[‘1.05’, ‘1.05’]	1.05	[‘1.05’, ‘1.05’]	1.05	[‘1.05’, ‘1.05’]	1.05	[‘1.05’, ‘1.05’]
12	ht{‘coeff’: 0.5, ‘Nfft’: 4096}	1.17	[‘1.16’, ‘1.18’]	1.28	[‘1.27’, ‘1.29’]	1.48	[‘1.46’, ‘1.49’]	1.78	[‘1.76’, ‘1.79’]	2.15	[‘2.13’, ‘2.18’]	2.59	[‘2.57’, ‘2.62’]
13	ht{‘coeff’: 1.0, ‘Nfft’: 4096}	1.19	[‘1.18’, ‘1.20’]	1.31	[‘1.30’, ‘1.32’]	1.52	[‘1.51’, ‘1.53’]	1.84	[‘1.82’, ‘1.85’]	2.23	[‘2.20’, ‘2.25’]	2.66	[‘2.63’, ‘2.68’]
14	ht{‘coeff’: 1.5, ‘Nfft’: 4096}	1.20	[‘1.19’, ‘1.21’]	1.33	[‘1.32’, ‘1.34’]	1.54	[‘1.52’, ‘1.55’]	1.83	[‘1.82’, ‘1.85’]	2.17	[‘2.15’, ‘2.19’]	2.57	[‘2.55’, ‘2.59’]
15	ht{‘coeff’: 2.0, ‘Nfft’: 4096}	1.21	[‘1.19’, ‘1.22’]	1.33	[‘1.32’, ‘1.34’]	1.52	[‘1.51’, ‘1.54’]	1.80	[‘1.78’, ‘1.81’]	2.08	[‘2.06’, ‘2.10’]	2.44	[‘2.41’, ‘2.46’]
16	ht{‘coeff’: 2.5, ‘Nfft’: 4096}	1.20	[‘1.19’, ‘1.21’]	1.32	[‘1.31’, ‘1.33’]	1.50	[‘1.49’, ‘1.51’]	1.75	[‘1.73’, ‘1.76’]	2.02	[‘2.01’, ‘2.04’]	2.35	[‘2.33’, ‘2.38’]
17	ht{‘coeff’: 3.0, ‘Nfft’: 4096}	1.20	[‘1.19’, ‘1.21’]	1.31	[‘1.30’, ‘1.32’]	1.47	[‘1.46’, ‘1.49’]	1.70	[‘1.68’, ‘1.71’]	1.99	[‘1.97’, ‘2.00’]	2.32	[‘2.30’, ‘2.34’]
18	ht{‘coeff’: 3.5, ‘Nfft’: 4096}	1.20	[‘1.18’, ‘1.21’]	1.31	[‘1.30’, ‘1.32’]	1.45	[‘1.44’, ‘1.46’]	1.66	[‘1.65’, ‘1.67’]	1.94	[‘1.93’, ‘1.96’]	2.32	[‘2.29’, ‘2.34’]
19	ht{‘coeff’: 4.0, ‘Nfft’: 4096}	1.19	[‘1.18’, ‘1.20’]	1.31	[‘1.30’, ‘1.32’]	1.44	[‘1.42’, ‘1.45’]	1.63	[‘1.62’, ‘1.64’]	1.90	[‘1.89’, ‘1.92’]	2.32	[‘2.29’, ‘2.35’]
20	ht{‘coeff’: 4.5, ‘Nfft’: 4096}	1.19	[‘1.18’, ‘1.21’]	1.31	[‘1.30’, ‘1.32’]	1.42	[‘1.41’, ‘1.43’]	1.61	[‘1.59’, ‘1.62’]	1.87	[‘1.85’, ‘1.88’]	2.30	[‘2.27’, ‘2.33’]
21	ht{‘coeff’: 5.0, ‘Nfft’: 4096}	1.19	[‘1.18’, ‘1.21’]	1.31	[‘1.30’, ‘1.32’]	1.41	[‘1.40’, ‘1.42’]	1.59	[‘1.57’, ‘1.60’]	1.85	[‘1.83’, ‘1.86’]	2.26	[‘2.23’, ‘2.30’]

Signal: cello [View Plot] [Get .csv]

	Method + Param	SNRin=-5dB (average)	SNRin=-5dB (CI)	SNRin=0dB (average)	SNRin=0dB (CI)	SNRin=5dB (average)	SNRin=5dB (CI)	SNRin=10dB (average)	SNRin=10dB (CI)	SNRin=15dB (average)	SNRin=15dB (CI)	SNRin=20dB (average)	SNRin=20dB (CI)
0	dt{‘scale_fun’: <function scale_fun_APF at 0x7accaf7ed870>}	1.28	[‘1.27’, ‘1.29’]	1.39	[‘1.38’, ‘1.40’]	1.60	[‘1.59’, ‘1.62’]	1.91	[‘1.89’, ‘1.93’]	2.35	[‘2.32’, ‘2.38’]	2.90	[‘2.86’, ‘2.93’]
1	dt{‘scale_fun’: <function scale_fun_Fvs at 0x7accb0468d30>}	1.25	[‘1.23’, ‘1.28’]	1.35	[‘1.32’, ‘1.37’]	1.55	[‘1.53’, ‘1.58’]	1.87	[‘1.84’, ‘1.89’]	2.29	[‘2.26’, ‘2.32’]	2.79	[‘2.75’, ‘2.84’]
2	dt{‘scale_fun’: <function scale_fun_F at 0x7accaf7ed7e0>}	1.32	[‘1.30’, ‘1.33’]	1.44	[‘1.43’, ‘1.44’]	1.64	[‘1.63’, ‘1.66’]	1.94	[‘1.92’, ‘1.95’]	2.33	[‘2.30’, ‘2.35’]	2.86	[‘2.83’, ‘2.90’]
3	dt{‘LB’: 1.0}	1.37	[‘1.36’, ‘1.39’]	1.46	[‘1.45’, ‘1.47’]	1.65	[‘1.64’, ‘1.66’]	1.93	[‘1.91’, ‘1.94’]	2.28	[‘2.27’, ‘2.30’]	2.80	[‘2.77’, ‘2.82’]
4	dt{‘LB’: 1.25}	1.36	[‘1.34’, ‘1.37’]	1.46	[‘1.44’, ‘1.47’]	1.65	[‘1.64’, ‘1.66’]	1.93	[‘1.91’, ‘1.94’]	2.30	[‘2.28’, ‘2.32’]	2.82	[‘2.79’, ‘2.85’]
5	dt{‘LB’: 1.5}	1.31	[‘1.30’, ‘1.32’]	1.43	[‘1.42’, ‘1.44’]	1.64	[‘1.62’, ‘1.65’]	1.93	[‘1.91’, ‘1.95’]	2.33	[‘2.30’, ‘2.35’]	2.87	[‘2.84’, ‘2.91’]
6	dt{‘LB’: 1.75}	1.14	[‘1.14’, ‘1.15’]	1.26	[‘1.25’, ‘1.27’]	1.49	[‘1.47’, ‘1.51’]	1.83	[‘1.80’, ‘1.86’]	2.26	[‘2.23’, ‘2.30’]	2.76	[‘2.72’, ‘2.81’]
7	dt{‘LB’: 2.0}	1.07	[‘1.07’, ‘1.08’]	1.17	[‘1.16’, ‘1.18’]	1.34	[‘1.32’, ‘1.36’]	1.59	[‘1.56’, ‘1.63’]	1.88	[‘1.83’, ‘1.92’]	2.12	[‘2.08’, ‘2.16’]
8	dt{‘LB’: 2.25}	1.02	[‘1.02’, ‘1.02’]	1.03	[‘1.03’, ‘1.03’]	1.05	[‘1.05’, ‘1.05’]	1.09	[‘1.08’, ‘1.10’]	1.17	[‘1.16’, ‘1.18’]	1.27	[‘1.26’, ‘1.29’]
9	dt{‘LB’: 2.5}	1.04	[‘1.03’, ‘1.05’]	1.03	[‘1.02’, ‘1.03’]	1.02	[‘1.02’, ‘1.02’]	1.02	[‘1.02’, ‘1.03’]	1.02	[‘1.02’, ‘1.02’]	1.03	[‘1.02’, ‘1.03’]
10	dt{‘LB’: 2.75}	1.04	[‘1.04’, ‘1.05’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]
11	dt{‘LB’: 3.0}	1.04	[‘1.04’, ‘1.05’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]
12	ht{‘coeff’: 0.5, ‘Nfft’: 4096}	1.37	[‘1.35’, ‘1.38’]	1.46	[‘1.45’, ‘1.46’]	1.64	[‘1.63’, ‘1.66’]	1.93	[‘1.91’, ‘1.94’]	2.30	[‘2.28’, ‘2.32’]	2.83	[‘2.80’, ‘2.86’]
13	ht{‘coeff’: 1.0, ‘Nfft’: 4096}	1.33	[‘1.32’, ‘1.35’]	1.43	[‘1.42’, ‘1.44’]	1.62	[‘1.61’, ‘1.63’]	1.92	[‘1.90’, ‘1.93’]	2.31	[‘2.29’, ‘2.33’]	2.86	[‘2.83’, ‘2.90’]
14	ht{‘coeff’: 1.5, ‘Nfft’: 4096}	1.31	[‘1.29’, ‘1.32’]	1.41	[‘1.40’, ‘1.42’]	1.61	[‘1.60’, ‘1.63’]	1.93	[‘1.91’, ‘1.95’]	2.36	[‘2.33’, ‘2.38’]	2.94	[‘2.90’, ‘2.97’]
15	ht{‘coeff’: 2.0, ‘Nfft’: 4096}	1.29	[‘1.27’, ‘1.30’]	1.40	[‘1.39’, ‘1.41’]	1.62	[‘1.60’, ‘1.63’]	1.96	[‘1.93’, ‘1.98’]	2.42	[‘2.39’, ‘2.45’]	3.02	[‘2.98’, ‘3.06’]
16	ht{‘coeff’: 2.5, ‘Nfft’: 4096}	1.28	[‘1.27’, ‘1.29’]	1.40	[‘1.39’, ‘1.42’]	1.64	[‘1.62’, ‘1.65’]	2.00	[‘1.97’, ‘2.02’]	2.48	[‘2.45’, ‘2.52’]	3.11	[‘3.06’, ‘3.15’]
17	ht{‘coeff’: 3.0, ‘Nfft’: 4096}	1.27	[‘1.26’, ‘1.28’]	1.41	[‘1.40’, ‘1.42’]	1.66	[‘1.64’, ‘1.68’]	2.04	[‘2.01’, ‘2.07’]	2.55	[‘2.51’, ‘2.58’]	3.19	[‘3.14’, ‘3.23’]
18	ht{‘coeff’: 3.5, ‘Nfft’: 4096}	1.24	[‘1.23’, ‘1.25’]	1.42	[‘1.41’, ‘1.44’]	1.69	[‘1.67’, ‘1.72’]	2.08	[‘2.05’, ‘2.11’]	2.61	[‘2.57’, ‘2.65’]	3.27	[‘3.22’, ‘3.32’]
19	ht{‘coeff’: 4.0, ‘Nfft’: 4096}	1.20	[‘1.19’, ‘1.22’]	1.43	[‘1.41’, ‘1.44’]	1.72	[‘1.69’, ‘1.75’]	2.12	[‘2.08’, ‘2.15’]	2.67	[‘2.63’, ‘2.71’]	3.35	[‘3.30’, ‘3.41’]
20	ht{‘coeff’: 4.5, ‘Nfft’: 4096}	1.16	[‘1.15’, ‘1.18’]	1.42	[‘1.40’, ‘1.44’]	1.74	[‘1.72’, ‘1.77’]	2.16	[‘2.12’, ‘2.19’]	2.74	[‘2.69’, ‘2.79’]	3.44	[‘3.38’, ‘3.50’]
21	ht{‘coeff’: 5.0, ‘Nfft’: 4096}	1.13	[‘1.12’, ‘1.14’]	1.39	[‘1.37’, ‘1.41’]	1.76	[‘1.74’, ‘1.79’]	2.20	[‘2.17’, ‘2.24’]	2.82	[‘2.77’, ‘2.87’]	3.53	[‘3.47’, ‘3.59’]

Signal: trumpet [View Plot] [Get .csv]

	Method + Param	SNRin=-5dB (average)	SNRin=-5dB (CI)	SNRin=0dB (average)	SNRin=0dB (CI)	SNRin=5dB (average)	SNRin=5dB (CI)	SNRin=10dB (average)	SNRin=10dB (CI)	SNRin=15dB (average)	SNRin=15dB (CI)	SNRin=20dB (average)	SNRin=20dB (CI)
0	dt{‘scale_fun’: <function scale_fun_APF at 0x7accaf7ed870>}	1.09	[‘1.09’, ‘1.09’]	1.20	[‘1.19’, ‘1.20’]	1.41	[‘1.40’, ‘1.43’]	1.75	[‘1.73’, ‘1.76’]	2.18	[‘2.15’, ‘2.20’]	2.71	[‘2.68’, ‘2.74’]
1	dt{‘scale_fun’: <function scale_fun_Fvs at 0x7accb0468d30>}	1.08	[‘1.08’, ‘1.09’]	1.19	[‘1.18’, ‘1.20’]	1.42	[‘1.40’, ‘1.44’]	1.77	[‘1.74’, ‘1.79’]	2.21	[‘2.18’, ‘2.25’]	2.70	[‘2.66’, ‘2.74’]
2	dt{‘scale_fun’: <function scale_fun_F at 0x7accaf7ed7e0>}	1.08	[‘1.08’, ‘1.09’]	1.18	[‘1.17’, ‘1.18’]	1.37	[‘1.36’, ‘1.38’]	1.68	[‘1.66’, ‘1.69’]	2.08	[‘2.07’, ‘2.10’]	2.61	[‘2.58’, ‘2.63’]
3	dt{‘LB’: 1.0}	1.07	[‘1.06’, ‘1.07’]	1.13	[‘1.13’, ‘1.14’]	1.28	[‘1.27’, ‘1.29’]	1.54	[‘1.53’, ‘1.55’]	1.90	[‘1.89’, ‘1.92’]	2.35	[‘2.33’, ‘2.37’]
4	dt{‘LB’: 1.25}	1.07	[‘1.07’, ‘1.07’]	1.14	[‘1.14’, ‘1.15’]	1.30	[‘1.29’, ‘1.31’]	1.58	[‘1.57’, ‘1.59’]	1.95	[‘1.93’, ‘1.96’]	2.42	[‘2.40’, ‘2.44’]
5	dt{‘LB’: 1.5}	1.09	[‘1.08’, ‘1.09’]	1.18	[‘1.18’, ‘1.19’]	1.38	[‘1.37’, ‘1.39’]	1.69	[‘1.68’, ‘1.71’]	2.10	[‘2.08’, ‘2.12’]	2.63	[‘2.61’, ‘2.66’]
6	dt{‘LB’: 1.75}	1.08	[‘1.08’, ‘1.09’]	1.21	[‘1.20’, ‘1.22’]	1.44	[‘1.42’, ‘1.46’]	1.77	[‘1.75’, ‘1.80’]	2.22	[‘2.19’, ‘2.26’]	2.70	[‘2.65’, ‘2.74’]
7	dt{‘LB’: 2.0}	1.04	[‘1.04’, ‘1.05’]	1.13	[‘1.12’, ‘1.14’]	1.29	[‘1.27’, ‘1.31’]	1.51	[‘1.49’, ‘1.54’]	1.73	[‘1.70’, ‘1.77’]	2.03	[‘1.99’, ‘2.07’]
8	dt{‘LB’: 2.25}	1.03	[‘1.03’, ‘1.04’]	1.03	[‘1.03’, ‘1.03’]	1.04	[‘1.04’, ‘1.04’]	1.06	[‘1.06’, ‘1.06’]	1.09	[‘1.08’, ‘1.09’]	1.10	[‘1.10’, ‘1.11’]
9	dt{‘LB’: 2.5}	1.03	[‘1.03’, ‘1.04’]	1.03	[‘1.03’, ‘1.04’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.04’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]
10	dt{‘LB’: 2.75}	1.04	[‘1.03’, ‘1.04’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]
11	dt{‘LB’: 3.0}	1.04	[‘1.03’, ‘1.04’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]	1.03	[‘1.03’, ‘1.03’]
12	ht{‘coeff’: 0.5, ‘Nfft’: 4096}	1.07	[‘1.07’, ‘1.07’]	1.14	[‘1.13’, ‘1.14’]	1.29	[‘1.28’, ‘1.29’]	1.56	[‘1.54’, ‘1.57’]	1.93	[‘1.91’, ‘1.94’]	2.41	[‘2.39’, ‘2.43’]
13	ht{‘coeff’: 1.0, ‘Nfft’: 4096}	1.07	[‘1.07’, ‘1.08’]	1.14	[‘1.14’, ‘1.15’]	1.30	[‘1.29’, ‘1.31’]	1.58	[‘1.57’, ‘1.60’]	1.97	[‘1.95’, ‘1.99’]	2.50	[‘2.48’, ‘2.53’]
14	ht{‘coeff’: 1.5, ‘Nfft’: 4096}	1.08	[‘1.07’, ‘1.08’]	1.15	[‘1.15’, ‘1.16’]	1.31	[‘1.30’, ‘1.32’]	1.59	[‘1.58’, ‘1.60’]	1.98	[‘1.96’, ‘2.00’]	2.57	[‘2.55’, ‘2.60’]
15	ht{‘coeff’: 2.0, ‘Nfft’: 4096}	1.08	[‘1.08’, ‘1.08’]	1.15	[‘1.15’, ‘1.16’]	1.32	[‘1.31’, ‘1.32’]	1.59	[‘1.58’, ‘1.60’]	1.99	[‘1.97’, ‘2.01’]	2.66	[‘2.63’, ‘2.69’]
16	ht{‘coeff’: 2.5, ‘Nfft’: 4096}	1.08	[‘1.08’, ‘1.08’]	1.15	[‘1.14’, ‘1.15’]	1.31	[‘1.31’, ‘1.32’]	1.59	[‘1.58’, ‘1.61’]	2.02	[‘2.00’, ‘2.04’]	2.78	[‘2.74’, ‘2.82’]
17	ht{‘coeff’: 3.0, ‘Nfft’: 4096}	1.08	[‘1.07’, ‘1.08’]	1.14	[‘1.14’, ‘1.15’]	1.31	[‘1.30’, ‘1.32’]	1.59	[‘1.58’, ‘1.60’]	2.10	[‘2.08’, ‘2.12’]	2.89	[‘2.84’, ‘2.94’]
18	ht{‘coeff’: 3.5, ‘Nfft’: 4096}	1.08	[‘1.07’, ‘1.08’]	1.14	[‘1.14’, ‘1.15’]	1.29	[‘1.28’, ‘1.30’]	1.59	[‘1.58’, ‘1.60’]	2.12	[‘2.10’, ‘2.15’]	3.05	[‘3.01’, ‘3.10’]
19	ht{‘coeff’: 4.0, ‘Nfft’: 4096}	1.07	[‘1.07’, ‘1.08’]	1.14	[‘1.14’, ‘1.15’]	1.28	[‘1.27’, ‘1.28’]	1.60	[‘1.59’, ‘1.61’]	2.15	[‘2.12’, ‘2.18’]	3.07	[‘3.03’, ‘3.12’]
20	ht{‘coeff’: 4.5, ‘Nfft’: 4096}	1.07	[‘1.07’, ‘1.07’]	1.14	[‘1.13’, ‘1.14’]	1.28	[‘1.27’, ‘1.29’]	1.57	[‘1.56’, ‘1.58’]	2.19	[‘2.15’, ‘2.23’]	3.10	[‘3.06’, ‘3.14’]
21	ht{‘coeff’: 5.0, ‘Nfft’: 4096}	1.06	[‘1.06’, ‘1.07’]	1.14	[‘1.13’, ‘1.14’]	1.28	[‘1.28’, ‘1.29’]	1.54	[‘1.53’, ‘1.56’]	2.25	[‘2.21’, ‘2.30’]	2.99	[‘2.95’, ‘3.04’]