General information |
| Label |
Value |
Description |
|
Name |
Wearable SELD FOA |
Full dataset name |
|
ID |
sounds/wearable_seld_foa
|
Datalist id for external indexing |
|
Abbreviation |
Wearable SELD FOA |
Official dataset abbreviation, e.g. one used in the original paper |
|
Provider |
NTT Media Intelligence Laboratories |
|
|
Year |
2022 |
Dataset release year |
|
Modalities |
Audio
|
Data modalities included in the dataset |
|
Collection name |
Wearable SELD |
Common name for all related datasets, used to group datasets coming from same source |
|
Research domain |
SELD
|
Related domains, e.g., Scenes, Mobile devices, Audio-visual, Open set, Ambient noise, Unlabelled, Multiple sensors, SED, SELD, Tagging, FL, Strong annotation, Weak annotation, Unlabelled, Multi-annotator |
|
Related datasets name |
|
|
|
License |
Non-commercial |
|
|
Download |
Download
(64.1 GB)
|
|
|
Companion site |
Site
|
Link to the companion site for the dataset |
|
Citation |
[Nagatomo2022] Wearable SELD dataset: Dataset for sound event localization and detection using wearable devices around head
|
|
Audio |
| Label |
Value |
Description |
|
Data |
|
|
Data type |
Audio
|
Possible values: Audio | Features |
|
|
File format |
|
|
|
File format type |
Constant
|
Possible values: Constant | Variable |
|
|
|
File format |
wav
|
Possible value: wav | aiff | flac | mp3 | aac | ogg |
|
|
|
Lossy compression |
No
|
is audio compressed in a lossy manner |
|
|
|
Sampling rate (kHz) |
48 kHz |
Sampling rate in kHz, possible values: 8 | 16 | 22.05 | 32 | 44.1 | 48 |
|
|
Channels |
|
|
|
Setup |
Ambisonic
|
Possible values: Mono | Stereo | Binaural | Ambisonic | Array | Multi-Channel | Variable |
|
|
|
Number of channels |
4 |
|
|
|
Material |
|
|
|
Source |
Original
|
Possible values: Original | Youtube | Freesound | Online | Crowdsourced | [Dataset name] |
|
Content |
|
|
Content type |
Freefield
|
Possible values: Freefield | Synthetic | Isolated |
|
|
Scene |
Constant
|
Is the scene class constant for single recording, possible values: Constant | Variable |
|
|
Event / Spatial location |
Constant
|
Possible values: Constant | Moving | Unknown |
|
Recording |
|
|
Setup |
Near-field
|
Possible values: Near-field | Far-field | Mixed | Uncontrolled | Unknown |
|
|
Setup count |
1 |
Amount of different recording setups (microphone and recording device) used |
|
|
Spot type |
Fixed
|
Possible values: Fixed | Moving | Unknown |
|
Files |
|
|
Count |
500 files |
Total number of files |
|
|
Total duration (minutes) |
500 min |
Total duration of the dataset in minutes |
|
|
File length |
Constant
|
Characterization of the file lengths, possible values: Constant | Quasi-constant | Variable |
|
|
File length (seconds) |
60 sec |
Approximate length of files |
Meta |
| Label |
Value |
Description |
|
Types |
Event
Spatial location
|
List of meta data types provided for the data, possible values: Event, Tag, Scene, Caption, Geolocation, Spatial location, Annotator, Timestamp, Presence, Proximity, etc. |
|
Event |
|
|
Classes |
12 |
Number of event classes |
|
|
Classes |
- organ
- piano
- toy train
- toy gun shot
- metallophone
- bicycle bell
- security buzzer
- shaker
- handclap
- woodblock
- shaking bell
- hit drum
|
|
|
|
Annotation |
|
|
|
Type |
Strong
|
Possible values: Strong | Weak | Location | None |
|
|
|
Source |
Synthetic
|
Possible values: Experts | Crowdsourced | Synthetic | Metadata | Automatic |
|
|
|
Annotations per item |
1 |
How many annotations there are available per item (possible multi-annotator setup) |
|
|
|
Labelled amount (%) |
100 % |
Percentage of all data, amount of data which is labelled |
|
|
|
Validated amount (%) |
100 % |
Percentage of all data, amount of data which is validated by human |
|
|
|
Strong annotations amount (%) |
100 % |
Percentage of all data, amount of data which has strong annotations |
|
|
Labeling |
|
|
|
Hierarchical |
No
|
|
|
|
Instance |
Cross-validation setup |
| Label |
Value |
Description |
|
|
Provided |
Yes
|
|
|
|
Folds |
1 |
|
|
|
Sets |
Train
Test
|
Set types provided in the split, possible values: Train | Test | Val | Dev | Eval |