Metadata
Library provides container dcase_util.containers.MetaDataContainer for handling meta data from most of the DCASE related
application areas: acoustic scene classification, event detection, and audio tagging.
In principal, meta data is a list containing meta item dictionaries, and it can be used like normal list.
Creating container
Initialize meta data container with acoustic scene list:
meta_container_scenes = dcase_util.containers.MetaDataContainer(
[
{
'filename': 'file1.wav',
'scene_label': 'office'
},
{
'filename': 'file2.wav',
'scene_label': 'street'
},
{
'filename': 'file3.wav',
'scene_label': 'home'
}
]
)
Initialize meta data container with sound event list:
meta_container_events = dcase_util.containers.MetaDataContainer(
[
{
'filename': 'file1.wav',
'event_label': 'speech',
'onset': 10.0,
'offset': 15.0,
},
{
'filename': 'file1.wav',
'event_label': 'footsteps',
'onset': 23.0,
'offset': 26.0,
},
{
'filename': 'file2.wav',
'event_label': 'speech',
'onset': 2.0,
'offset': 5.0,
}
]
)
Initialize meta data container with tags:
meta_container_tags = dcase_util.containers.MetaDataContainer(
[
{
'filename': 'file1.wav',
'tags': ['cat','dog']
},
{
'filename': 'file2.wav',
'tags': ['dog']
},
{
'filename': 'file3.wav',
'tags': ['dog','horse']
}
]
)
Show content:
meta_container_scenes.show()
# MetaDataContainer :: Class
# Items : 3
# Unique
# Files : 3
# Scene labels : 3
# Event labels : 0
# Tags : 0
#
# Scene statistics
# Scene label Count
# -------------------- ------
# home 1
# office 1
# street 1
meta_container_events.show()
# MetaDataContainer :: Class
# Items : 3
# Unique
# Files : 2
# Scene labels : 0
# Event labels : 2
# Tags : 0
#
# Event statistics
# Event label Count Tot. Length Avg. Length
# -------------------- ------ ----------- -----------
# footsteps 1 3.00 3.00
# speech 2 8.00 4.00
meta_container_tags.show()
# MetaDataContainer :: Class
# Items : 3
# Unique
# Files : 3
# Scene labels : 0
# Event labels : 0
# Tags : 3
#
# Tag statistics
# Tag Count
# -------------------- ------
# cat 1
# dog 3
# horse 1
Show content and each meta data item:
meta_container_scenes.show_all()
# MetaDataContainer :: Class
# Items : 3
# Unique
# Files : 3
# Scene labels : 3
# Event labels : 0
# Tags : 0
#
# Meta data
# Source Onset Offset Scene Event Tags Identifier
# -------------------- ------ ------ --------------- --------------- --------------- -----
# file1.wav - - office - - -
# file2.wav - - street - - -
# file3.wav - - home - - -
#
# Scene statistics
# Scene label Count
# -------------------- ------
# home 1
# office 1
# street 1
meta_container_events.show_all()
# MetaDataContainer :: Class
# Items : 3
# Unique
# Files : 2
# Scene labels : 0
# Event labels : 2
# Tags : 0
#
# Meta data
# Source Onset Offset Scene Event Tags Identifier
# -------------------- ------ ------ --------------- --------------- --------------- -----
# file1.wav 10.00 15.00 - speech - -
# file1.wav 23.00 26.00 - footsteps - -
# file2.wav 2.00 5.00 - speech - -
#
# Event statistics
# Event label Count Tot. Length Avg. Length
# -------------------- ------ ----------- -----------
# footsteps 1 3.00 3.00
# speech 2 8.00 4.00
Loading and saving
Save meta data to file:
meta_container_events.save(filename='events.txt')
Load meta data from annotation file:
meta_container_events = dcase_util.containers.MetaDataContainer().load(
filename='events.txt'
)
Accessing data
Getting audio files mentioned in the meta data and their count:
print(meta_container_events.unique_files)
# ['file1.wav', 'file2.wav']
print(meta_container_events.file_count)
# 2
Getting unique scene labels and their count:
print(meta_container_scenes.unique_scene_labels)
# ['home', 'office', 'street']
print(meta_container_scenes.scene_label_count)
# 3
Getting unique event labels used in the meta data and their count:
print(meta_container_events.unique_event_labels)
# ['footsteps', 'speech']
print(meta_container_scenes.event_label_count)
# 2
Getting unique tags used in the meta data and their count:
print(meta_container_tags.unique_tags)
# ['cat', 'dog', 'horse']
print(meta_container_tags.tag_count)
# 3
Filtering
Filtering meta data based on filename:
filtered = meta_container_events.filter(filename='file1.wav')
filtered.show_all()
# MetaDataContainer :: Class
# Items : 2
# Unique
# Files : 1
# Scene labels : 0
# Event labels : 2
# Tags : 0
#
# Meta data
# Source Onset Offset Scene Event Tags Identifier
# -------------------- ------ ------ --------------- --------------- --------------- -----
# file1.wav 10.00 15.00 - speech - -
# file1.wav 23.00 26.00 - footsteps - -
#
# Event statistics
# Event label Count Tot. Length Avg. Length
# -------------------- ------ ----------- -----------
# footsteps 1 3.00 3.00
# speech 1 5.00 5.00
Filtering meta data based on event label:
filtered = meta_container_events.filter(event_label='speech')
filtered.show_all()
# MetaDataContainer :: Class
# Items : 2
# Unique
# Files : 2
# Scene labels : 0
# Event labels : 1
# Tags : 0
#
# Meta data
# Source Onset Offset Scene Event Tags Identifier
# -------------------- ------ ------ --------------- --------------- --------------- -----
# file1.wav 10.00 15.00 - speech - -
# file2.wav 2.00 5.00 - speech - -
#
# Event statistics
# Event label Count Tot. Length Avg. Length
# -------------------- ------ ----------- -----------
# speech 2 8.00 4.00
Filtering meta data based on file and event label:
filtered = meta_container_events.filter(filename='file1.wav', event_label='speech')
filtered.show_all()
# MetaDataContainer :: Class
# Items : 1
# Unique
# Files : 1
# Scene labels : 0
# Event labels : 1
# Tags : 0
#
# Meta data
# Source Onset Offset Scene Event Tags Identifier
# -------------------- ------ ------ --------------- --------------- --------------- -----
# file1.wav 10.00 15.00 - speech - -
#
# Event statistics
# Event label Count Tot. Length Avg. Length
# -------------------- ------ ----------- -----------
# speech 1 5.00 5.00
Filtering based on time segment, and make segment start new zero time:
filtered = meta_container_events.filter_time_segment(filename='file1.wav', start=12, stop=24)
filtered.show_all()
# MetaDataContainer :: Class
# Items : 2
# Unique
# Files : 1
# Scene labels : 0
# Event labels : 2
# Tags : 0
#
# Meta data
# Source Onset Offset Scene Event Tags Identifier
# -------------------- ------ ------ --------------- --------------- --------------- -----
# file1.wav 0.00 3.00 - speech - -
# file1.wav 11.00 12.00 - footsteps - -
#
# Event statistics
# Event label Count Tot. Length Avg. Length
# -------------------- ------ ----------- -----------
# footsteps 1 1.00 1.00
# speech 1 3.00 3.00
Processing
Add time offset to the onset and offset set in the meta data items:
meta_container_events.add_time(time=10)
meta_container_events.show_all()
# MetaDataContainer :: Class
# Items : 3
# Unique
# Files : 2
# Scene labels : 0
# Event labels : 2
# Tags : 0
#
# Meta data
# Source Onset Offset Scene Event Tags Identifier
# -------------------- ------ ------ --------------- --------------- --------------- -----
# file1.wav 20.00 25.00 - speech - -
# file1.wav 33.00 36.00 - footsteps - -
# file2.wav 12.00 15.00 - speech - -
#
# Event statistics
# Event label Count Tot. Length Avg. Length
# -------------------- ------ ----------- -----------
# footsteps 1 3.00 3.00
# speech 2 8.00 4.00
Remove very short events and merge events with small gap between them (same event label):
meta_container_events = dcase_util.containers.MetaDataContainer(
[
{
'filename': 'file1.wav',
'event_label': 'speech',
'onset': 1.0,
'offset': 2.0,
},
{
'filename': 'file1.wav',
'event_label': 'speech',
'onset': 2.05,
'offset': 2.5,
},
{
'filename': 'file1.wav',
'event_label': 'speech',
'onset': 5.1,
'offset': 5.15,
},
]
)
processed = meta_container_events.process_events(minimum_event_length=0.2, minimum_event_gap=0.1)
processed.show_all()
# MetaDataContainer :: Class
# Items : 1
# Unique
# Files : 1
# Scene labels : 0
# Event labels : 1
# Tags : 0
#
# Meta data
# Source Onset Offset Scene Event Tags Identifier
# -------------------- ------ ------ --------------- --------------- --------------- -----
# file1.wav 1.00 2.50 - speech - -
#
# Event statistics
# Event label Count Tot. Length Avg. Length
# -------------------- ------ ----------- -----------
# speech 1 1.50 1.50
Event roll
Converting event list to event roll (binary matrix with event activity):
meta_container_events = dcase_util.containers.MetaDataContainer(
[
{
'filename': 'file1.wav',
'event_label': 'speech',
'onset': 1.0,
'offset': 2.0,
},
{
'filename': 'file1.wav',
'event_label': 'speech',
'onset': 2.05,
'offset': 2.5,
},
{
'filename': 'file1.wav',
'event_label': 'speech',
'onset': 5.1,
'offset': 5.15,
},
{
'filename': 'file1.wav',
'event_label': 'footsteps',
'onset': 3.1,
'offset': 4.15,
},
{
'filename': 'file1.wav',
'event_label': 'dog',
'onset': 2.6,
'offset': 3.6,
},
]
)
event_roll = meta_container_events.to_event_roll()
# Plot
event_roll.plot()
(Source code, png, hires.png, pdf)
