Remove banca filelist based since bob.db.banca exists, fixes #9

bob/bio/spear/config/database/banca/G/dev/for_models.lst

-1001_f_g1_s01_1001_en_1 1001 1001
-1002_f_g1_s01_1002_en_1 1002 1002
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-1007_f_g1_s01_1007_en_1 1007 1007
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-1013_f_g1_s01_1013_en_1 1013 1013
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-1028_m_g1_s01_1028_en_1 1028 1028
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-1031_m_g1_s01_1031_en_1 1031 1031
-1032_m_g1_s01_1032_en_1 1032 1032
-1033_m_g1_s01_1033_en_1 1033 1033
-1034_m_g1_s01_1034_en_1 1034 1034
-1035_m_g1_s01_1035_en_1 1035 1035
-1036_m_g1_s01_1036_en_1 1036 1036
-1037_m_g1_s01_1037_en_1 1037 1037
-1038_m_g1_s01_1038_en_1 1038 1038
-1039_m_g1_s01_1039_en_1 1039 1039
-1001_f_g1_s05_1001_en_1 1001 1001
-1002_f_g1_s05_1002_en_1 1002 1002
-1003_f_g1_s05_1003_en_1 1003 1003
-1004_f_g1_s05_1004_en_1 1004 1004
-1005_f_g1_s05_1005_en_1 1005 1005
-1006_f_g1_s05_1006_en_1 1006 1006
-1007_f_g1_s05_1007_en_1 1007 1007
-1008_f_g1_s05_1008_en_1 1008 1008
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-1010_f_g1_s05_1010_en_1 1010 1010
-1011_f_g1_s05_1011_en_1 1011 1011
-1012_f_g1_s05_1012_en_1 1012 1012
-1013_f_g1_s05_1013_en_1 1013 1013
-1027_m_g1_s05_1027_en_1 1027 1027
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-1033_m_g1_s05_1033_en_1 1033 1033
-1034_m_g1_s05_1034_en_1 1034 1034
-1035_m_g1_s05_1035_en_1 1035 1035
-1036_m_g1_s05_1036_en_1 1036 1036
-1037_m_g1_s05_1037_en_1 1037 1037
-1038_m_g1_s05_1038_en_1 1038 1038
-1039_m_g1_s05_1039_en_1 1039 1039
-1001_f_g1_s09_1001_en_1 1001 1001
-1002_f_g1_s09_1002_en_1 1002 1002
-1003_f_g1_s09_1003_en_1 1003 1003
-1004_f_g1_s09_1004_en_1 1004 1004
-1005_f_g1_s09_1005_en_1 1005 1005
-1006_f_g1_s09_1006_en_1 1006 1006
-1007_f_g1_s09_1007_en_1 1007 1007
-1008_f_g1_s09_1008_en_1 1008 1008
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-1010_f_g1_s09_1010_en_1 1010 1010
-1011_f_g1_s09_1011_en_1 1011 1011
-1012_f_g1_s09_1012_en_1 1012 1012
-1013_f_g1_s09_1013_en_1 1013 1013
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-1028_m_g1_s09_1028_en_1 1028 1028
-1029_m_g1_s09_1029_en_1 1029 1029
-1030_m_g1_s09_1030_en_1 1030 1030
-1031_m_g1_s09_1031_en_1 1031 1031
-1032_m_g1_s09_1032_en_1 1032 1032
-1033_m_g1_s09_1033_en_1 1033 1033
-1034_m_g1_s09_1034_en_1 1034 1034
-1035_m_g1_s09_1035_en_1 1035 1035
-1036_m_g1_s09_1036_en_1 1036 1036
-1037_m_g1_s09_1037_en_1 1037 1037
-1038_m_g1_s09_1038_en_1 1038 1038
-1039_m_g1_s09_1039_en_1 1039 1039

bob/bio/spear/config/database/banca/G/eval/for_models.lst

-1014_f_g2_s01_1014_en_1 1014 1014
-1015_f_g2_s01_1015_en_1 1015 1015
-1016_f_g2_s01_1016_en_1 1016 1016
-1017_f_g2_s01_1017_en_1 1017 1017
-1018_f_g2_s01_1018_en_1 1018 1018
-1019_f_g2_s01_1019_en_1 1019 1019
-1020_f_g2_s01_1020_en_1 1020 1020
-1021_f_g2_s01_1021_en_1 1021 1021
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-1024_f_g2_s01_1024_en_1 1024 1024
-1025_f_g2_s01_1025_en_1 1025 1025
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-1014_f_g2_s05_1014_en_1 1014 1014
-1015_f_g2_s05_1015_en_1 1015 1015
-1016_f_g2_s05_1016_en_1 1016 1016
-1017_f_g2_s05_1017_en_1 1017 1017
-1018_f_g2_s05_1018_en_1 1018 1018
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-1020_f_g2_s05_1020_en_1 1020 1020
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-1022_f_g2_s05_1022_en_1 1022 1022
-1023_f_g2_s05_1023_en_1 1023 1023
-1024_f_g2_s05_1024_en_1 1024 1024
-1025_f_g2_s05_1025_en_1 1025 1025
-1026_f_g2_s05_1026_en_1 1026 1026
-1040_m_g2_s05_1040_en_1 1040 1040
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-1014_f_g2_s09_1014_en_1 1014 1014
-1015_f_g2_s09_1015_en_1 1015 1015
-1016_f_g2_s09_1016_en_1 1016 1016
-1017_f_g2_s09_1017_en_1 1017 1017
-1018_f_g2_s09_1018_en_1 1018 1018
-1019_f_g2_s09_1019_en_1 1019 1019
-1020_f_g2_s09_1020_en_1 1020 1020
-1021_f_g2_s09_1021_en_1 1021 1021
-1022_f_g2_s09_1022_en_1 1022 1022
-1023_f_g2_s09_1023_en_1 1023 1023
-1024_f_g2_s09_1024_en_1 1024 1024
-1025_f_g2_s09_1025_en_1 1025 1025
-1026_f_g2_s09_1026_en_1 1026 1026
-1040_m_g2_s09_1040_en_1 1040 1040
-1041_m_g2_s09_1041_en_1 1041 1041
-1042_m_g2_s09_1042_en_1 1042 1042
-1043_m_g2_s09_1043_en_1 1043 1043
-1044_m_g2_s09_1044_en_1 1044 1044
-1045_m_g2_s09_1045_en_1 1045 1045
-1046_m_g2_s09_1046_en_1 1046 1046
-1047_m_g2_s09_1047_en_1 1047 1047
-1048_m_g2_s09_1048_en_1 1048 1048
-1049_m_g2_s09_1049_en_1 1049 1049
-1050_m_g2_s09_1050_en_1 1050 1050
-1051_m_g2_s09_1051_en_1 1051 1051
-1052_m_g2_s09_1052_en_1 1052 1052

bob/bio/spear/config/database/banca/G/norm/train_optional_world_1.lst

-9001_f_wm_s01_9001_en_1 9001
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-9059_f_wm_s09_9060_en_1 9059

bob/bio/spear/config/database/banca/G/norm/train_optional_world_2.lst

-9001_f_wm_s01_9001_en_1 9001
-9001_f_wm_s01_9002_en_1 9001
-9003_m_wm_s01_9003_en_1 9003
-9003_m_wm_s01_9004_en_1 9003
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-9007_m_wm_s01_9007_en_1 9007
-9007_m_wm_s01_9008_en_1 9007
-9009_m_wm_s01_9009_en_1 9009
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-9011_m_wm_s01_9011_en_1 9011
-9011_m_wm_s01_9012_en_1 9011
-9013_f_wm_s01_9013_en_1 9013
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-9025_m_wm_s05_9026_en_1 9025
-9027_m_wm_s05_9027_en_1 9027
-9027_m_wm_s05_9028_en_1 9027
-9029_f_wm_s05_9029_en_1 9029
-9029_f_wm_s05_9030_en_1 9029
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-9037_f_wm_s05_9038_en_1 9037
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-9055_f_wm_s09_9055_en_1 9055
-9055_f_wm_s09_9056_en_1 9055
-9057_m_wm_s09_9057_en_1 9057
-9057_m_wm_s09_9058_en_1 9057
-9059_f_wm_s09_9059_en_1 9059
-9059_f_wm_s09_9060_en_1 9059

bob/bio/spear/config/database/banca/G/norm/train_world.lst

-9001_f_wm_s01_9001_en_1 9001
-9001_f_wm_s01_9002_en_1 9001
-9003_m_wm_s01_9003_en_1 9003
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bob/bio/spear/config/database/banca_audio_G.py

-#!/usr/bin/env python
-
-import pkg_resources
-import bob.bio.base
-from bob.bio.spear.database import AudioBioFile
-
-banca_wav_directory = "[YOUR_BANCA_WAV_DIRECTORY]"
-
-database = bob.bio.base.database.FileListBioDatabase(pkg_resources.resource_filename('bob.bio.spear', 'config/database/banca'),
-                                                     'banca',
-                                                     bio_file_class=AudioBioFile,
-                                                     protocol='G',
-                                                     original_directory=banca_wav_directory,
-                                                     original_extension=".wav")

bob/bio/spear/test/test_databases.py

@@ -103,12 +103,3 @@ def test_timit():
     except IOError as e:
         raise SkipTest(
             "The database could not queried; probably the db.sql3 file is missing. Here is the error: '%s'" % e)
-
-
-def test_banca():
-    database = bob.bio.base.load_resource('banca-audio', 'database', preferred_package='bob.bio.spear')
-    try:
-        check_database(database, groups=('dev', 'eval'))
-    except IOError as e:
-        raise SkipTest(
-            "The database could not queried; probably the db.sql3 file is missing. Here is the error: '%s'" % e)

doc/baselines.rst

@@ -90,11 +90,13 @@ Particularly, here we can enable ROC curves, DET plots, CMC curves and the compu
 Experiments on different databases
 --------------------------------------------------------
 
-To make you more familiar with the tool, we provide you examples of different toolchains applied on different databases: Voxforge, BANCA, TIMIT, MOBIO, and NIST SRE 2012.
+To make you more familiar with the tool, we provide you examples of different toolchains applied on different databases: Voxforge, TIMIT, MOBIO, and NIST SRE 2012.
 
+1. Voxforge dataset
+~~~~~~~~~~~~~~~~~~~
 `Voxforge`_ is a free database used in free speech recognition engines. We randomly selected a small part of the english corpus (< 1GB).  It is used as a toy example for our speaker recognition tool since experiment can be easily run on a local machine, and the results can be obtained in a reasonnable amount of time (< 2h).
 
-Unlike TIMIT and BANCA, this dataset is completely free of charge.
+Unlike TIMIT, this dataset is completely free of charge.
 
 More details about how to download the audio files used in our experiments, and how the data is split into Training, Development and Evaluation set can be found here::
 
@@ -161,21 +163,7 @@ The scoring computation can also be done using **PLDA**::
 
 Note that in the previous examples, our goal is not to optimize the parameters on the DEV set but to provide examples of use.
 
-2. BANCA dataset
-~~~~~~~~~~~~~~~~
-`BANCA`_ is a simple bimodal database with relatively clean data. The results are already very good with a simple baseline UBM-GMM system. An example of use can be::
-
-  $ bin/verify.py -vv -d banca-audio -p energy-2gauss -e mfcc-60 -a gmm-banca -s banca_G --groups {dev,eval}
-
-The configuration in this example is similar to the previous one with the only difference of using the regular LLR instead of its linear approximation.
-
-Here is the performance of this system:
-
-* ``DEV: EER = 0.91%``
-* ``EVAL: EER = 0.75%``
-
-
-3. TIMIT dataset
+2. TIMIT dataset
 ~~~~~~~~~~~~~~~~
 `TIMIT`_ is one of the oldest databases (year 1993) used to evaluate speaker recognition systems. In the following example, the processing is done on the development set, and LFCC features are used::
 
@@ -186,7 +174,7 @@ Here is the performance of the system on the Development set:
 * ``DEV: EER = 2.68%``
 
 
-4. MOBIO dataset
+3. MOBIO dataset
 ~~~~~~~~~~~~~~~~
 This is a more challenging database. The noise and the short duration of the segments make the task of speaker recognition relatively difficult. The following experiment on male group (Mobile-0) uses the 4Hz modulation energy based VAD, and the ISV (with dimU=50) modelling technique::
 
@@ -200,7 +188,7 @@ Here is the performance of this system:
 To generate the results presented in the ICASSP 2014 paper, please check the script included in the `icassp` folder of the toolbox.
 Note that the MOBIO dataset has different protocols, and that are all implemented in `bob.db.mobio`_. But in this toolbox, we provide separately mobile-0 protocol (into filelist format) for simplicity.
 
-5. NIST SRE 2012
+4. NIST SRE 2012
 ~~~~~~~~~~~~~~~~~~
 We first invite you to read the paper describing our system submitted to the NIST SRE 2012 Evaluation. The protocols on the development set are the results of a joint work by the I4U group. To reproduce the results, please check this dedicated package::
 

doc/links.rst

@@ -21,7 +21,6 @@
 .. _virtualbox: https://www.virtualbox.org
 .. _hdf5: http://www.hdfgroup.org/HDF5
 .. _Voxforge: http://www.voxforge.org/
-.. _BANCA: http://www.ee.surrey.ac.uk/CVSSP/banca/
 .. _TIMIT: http://www.ldc.upenn.edu/Catalog/catalogEntry.jsp?catalogId=LDC93S1
 .. _logistic regression: http://en.wikipedia.org/wiki/Logistic_regression
 .. _spear: https://pypi.python.org/pypi/bob.bio.spear

setup.py

@@ -100,7 +100,6 @@ setup(
     entry_points = {
 
       'bob.bio.database': [
-        'banca-audio      = bob.bio.spear.config.database.banca_audio_G:database',
         'timit            = bob.bio.spear.config.database.timit:database',
         'mobio-audio-male       = bob.bio.spear.config.database.mobio_audio_male:database',
         'mobio-audio-female       = bob.bio.spear.config.database.mobio_audio_female:database',