dcase_util.datasets.DCASE2018_Task5_DevelopmentSet

class dcase_util.datasets.DCASE2018_Task5_DevelopmentSet(storage_name='DCASE18-Task5-development', data_path=None, included_content_types=None, **kwargs)

Task 5, Monitoring of domestic activities based on multi-channel acoustics, development set

This dataset is a derivative of the SINS database:

Dekkers G., Lauwereins S., Thoen B., Adhana M., Brouckxon H., Van den Bergh B., van Waterschoot T., Vanrumste B., Verhelst M., Karsmakers P. (2017). The SINS database for detection of daily activities in a home environment using an Acoustic Sensor Network. Detection and Classification of Acoustic Scenes and Events 2017 (accepted). DCASE Workshop. München, Germany, 16-17 November 2017. A subset is used for “DCASE2018 - Task 5, Monitoring of domestic activities based on multi-channel acoustics”

Constructor

Parameters

storage_namestr

Name to be used when storing dataset on disk

data_pathstr

Root path where the dataset is stored. If None, os.path.join(tempfile.gettempdir(), ‘dcase_util_datasets’) is used.

included_content_typeslist of str or str

Indicates what content type should be processed. One or multiple from [‘all’, ‘audio’, ‘meta’, ‘code’, ‘documentation’]. If None given, [‘all’] is used. Parameter can be also comma separated string.

__init__(storage_name='DCASE18-Task5-development', data_path=None, included_content_types=None, **kwargs)

Constructor

Parameters

storage_namestr

Name to be used when storing dataset on disk

data_pathstr

Root path where the dataset is stored. If None, os.path.join(tempfile.gettempdir(), ‘dcase_util_datasets’) is used.

included_content_typeslist of str or str

Indicates what content type should be processed. One or multiple from [‘all’, ‘audio’, ‘meta’, ‘code’, ‘documentation’]. If None given, [‘all’] is used. Parameter can be also comma separated string.

Methods

__init__([storage_name, data_path, ...])	Constructor
absolute_to_relative_path(path)	Converts absolute path into relative path.
check_filelist()	Generates hash from file list and check does it matches with one saved in filelist.hash.
check_metadata()	Checking meta data and cross-validation setup.
dataset_bytes()	Total download size of the dataset in bytes.
dataset_size_on_disk()	Total size of the dataset currently stored locally.
dataset_size_string()	Total download size of the dataset in a string.
debug_packages([local_check, remote_check])	Debug remote packages associated to the dataset.
download_packages(**kwargs)	Download dataset packages over the internet to the local path
eval([fold, absolute_paths])	List of evaluation items.
eval_files([fold, absolute_paths])	List of evaluation files.
evaluation_setup_filename([setup_part, ...])	Evaluation setup filename generation.
event_label_count(**kwargs)	Number of unique event labels in the meta data.
event_labels(**kwargs)	List of unique event labels in the meta data.
extract_packages(**kwargs)	Extract the dataset packages
file_error_meta(filename)	Error meta data for given file
file_features(filename)	Pre-calculated acoustic features for given file
file_meta(filename)	Meta data for given file
folds([mode])	List of fold ids
initialize()	Initialize the dataset, download, extract files and prepare the dataset for the usage.
load()	Load dataset meta data and cross-validation sets into the container.
load_crossvalidation_data()	Load cross-validation into the container.
load_meta()	Load meta data into the container.
log([show_meta])	Log dataset information.
prepare()	Prepare dataset for the usage.
process_meta_container(container)	Process meta container.
process_meta_item(item[, absolute_path])	Process single meta data item
relative_to_absolute_path(path)	Converts relative path into absolute path.
scene_label_count()	Number of unique scene labels in the meta data.
scene_labels()	List of unique scene labels in the meta data.
show([mode, indent, show_meta])	Show dataset information.
tag_count()	Number of unique audio tags in the meta data.
tags()	List of unique audio tags in the meta data.
test([fold, absolute_paths])	List of testing items.
test_files([fold, absolute_paths])	List of testing files.
train([fold, absolute_paths])	List of training items.
train_files([fold, absolute_paths])	List of training files.
validation_files_balanced([fold, ...])	List of validation files randomly selecting while maintaining data balance.
validation_files_dataset([fold])	List of validation files delivered by the dataset.
validation_files_random([fold, ...])	List of validation files selected randomly from the training material.
validation_split([fold, training_meta, ...])	List of validation files.

Attributes

audio_file_count	Get number of audio files in dataset
audio_files	Get all audio files in the dataset
error_meta	Get audio error meta data for dataset.
error_meta_count	Number of error meta data items.
fold_count	Number of fold in the evaluation setup.
logger
meta	Get meta data for dataset.
meta_count	Number of meta data items.