dcase_util.datasets.TAUUrbanAcousticScenes_2019_Mobile_LeaderboardSet

class dcase_util.datasets.TAUUrbanAcousticScenes_2019_Mobile_LeaderboardSet(storage_name='TAU-urban-acoustic-scenes-2019-mobile-leaderboard', data_path=None, included_content_types=None, **kwargs)

TAU Urban Acoustic Scenes 2019 Mobile Leaderboard dataset

This dataset is used in DCASE2019 - Task 1, Acoustic scene classification / Subtask B / Leaderboard

Constructor

Parameters

storage_namestr

Name to be used when storing dataset on disk Default value ‘TAU-urban-acoustic-scenes-2019-mobile-leaderboard’

data_pathstr

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

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. Default value None

__init__(storage_name='TAU-urban-acoustic-scenes-2019-mobile-leaderboard', data_path=None, included_content_types=None, **kwargs)

Constructor

Parameters

storage_namestr

Name to be used when storing dataset on disk Default value ‘TAU-urban-acoustic-scenes-2019-mobile-leaderboard’

data_pathstr

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

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. Default value None

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.