dcase_util.datasets.TUTAcousticScenes_2016_EvaluationSet

class dcase_util.datasets.TUTAcousticScenes_2016_EvaluationSet(storage_name='TUT-acoustic-scenes-2016-evaluation', data_path=None, included_content_types=None, **kwargs)[source]

TUT Acoustic scenes 2016 evaluation dataset

This dataset is used in DCASE2016 - Task 1, Acoustic scene classification

Constructor

Parameters
storage_namestr

Name to be used when storing dataset on disk Default value ‘TUT-acoustic-scenes-2016-evaluation’

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='TUT-acoustic-scenes-2016-evaluation', data_path=None, included_content_types=None, **kwargs)[source]

Constructor

Parameters
storage_namestr

Name to be used when storing dataset on disk Default value ‘TUT-acoustic-scenes-2016-evaluation’

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.