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---
task_categories:
- audio-classification
license: cc
tags:
- bird classification
- passive acoustic monitoring
---
## Dataset Description
- **Repository:** [https://github.com/s3prl/s3prl](https://github.com/s3prl/s3prl)
- **Paper:** [GADME](https://arxiv.org/))
- **Point of Contact:** [Lukas Rauch](mailto:lukas.rauch@uni-kassel.de)
### Dataset Summary
We present the GADME benchmark that covers a comprehensive range of avian monitoring datasets.
We offer a static set of evaluation datasets and a varied collection of training datasets, enabling the application of diverse methodologies
### Datasets
##### Train
- Exclusively using focal audio data from Xeno-Canto (XC) with quality ratings A, B, C and excluding all recordings that are CC-ND.
- Each dataset is tailored for specific target species identified in soundscape files.
- We offer detected events and corresponding cluster assignments to identify bird sounds in each recording.
- We provide the full recordings from XC! These can generate multiple samples from a single instance.
##### Test
- Only soundscape data sourced from Zenodo.
- We provide the full recording with the complete label set and specified bounding boxes.
- This dataset excludes recordings that do not contain bird calls ("no_call").
- Task: Multiclass ("ebird_code")
##### Test_5s
- Only soundscape data from Zenodo formatted acoording to the Kaggle evaluation scheme.
- Each recording is segmented into 5-second intervals without overlaps.
- This contains the "no_call" class.
- Task: Multilabel ("ebird_code_multilabel")
#### Subsets
Numbers need to be updated
| | train | test | test_5s | size (GB) | #classes |
|----------------------------|--------:|-----------:|--------:|-----------:|-------------:|
| HSN (high_sierras) | 5,197 | 10,296 | 12,000 | 6.2 | 21 |
| NBP (nips) | 24,327 | 5,493 | 563 | wip | 51 |
| NES (columbia_costa_rica) | 15,157 | 6,952 | 24,480 | 14 | 89 |
| PER (amazon_basin) | 15,679 | 14,798 | 15,120 | 10 | 132 |
| POW (powdermill_nature) | 13,922 | 16,052 | 4,560 | 16 | 48 |
| SNE (sierra_nevada) | 18,307 | 20,147 | 23,756 | 22 | 56 |
| SSW (sapsucker_woods) | 26,487 | 50,760 | 205,200| 36 | 81 |
| UHH (hawaiian_islands) | 3,427 | 59,583 | 36,637 | 5.1 | 25 tr, 27 te |
| XCM (xenocanto) | 80,012 | x | x | 83 | 409 |
| XCL (xenocanto) | 492,676| x | x | 485 | 9,734 |
#### FEATURES
```python
{
"audio": datasets.Audio(sampling_rate=32_000, mono=True, decode=True),
"filepath": datasets.Value("string"),
"start_time": datasets.Value("float64"),
"end_time": datasets.Value("float64"),
"low_freq": datasets.Value("int64"),
"high_freq": datasets.Value("int64"),
"ebird_code": datasets.ClassLabel(names=class_list),
"ebird_code_multilabel": datasets.Sequence(datasets.ClassLabel(names=["no_call"] + class_list)),
"ebird_code_secondary": datasets.Sequence(datasets.Value("string")),
"call_type": datasets.Value("string"),
"sex": datasets.Value("string"),
"lat": datasets.Value("float64"),
"long": datasets.Value("float64"),
"length": datasets.Value("int64"),
"microphone": datasets.Value("string"),
"license": datasets.Value("string"),
"source": datasets.Value("string"),
"local_time": datasets.Value("string"),
"detected_events": datasets.Sequence(datasets.Sequence(datasets.Value("float64"))),
"event_cluster": datasets.Sequence(datasets.Value("int64")),
"quality": datasets.Value("string"),
"recordist": datasets.Value("string")
})
```
```python
EXAMPLE TRAIN
{'audio': {'path': '.ogg',
'array': array([ 6.24680333e-02, 7.57145062e-02, 4.91199419e-02, ...,
-2.04162002e-02, 8.73558223e-03, -6.23320229e-05]),
'sampling_rate': 32000},
'filepath': '.ogg',
'start_time': None,
'end_time': None,
'low_freq': None,
'high_freq': None,
'ebird_code': 1,
'ebird_code_multiclass': None,
'call_type': 'call',
'sex': None,
'lat': 22.2029,
'long': -159.473,
'microphone': 'focal',
'license': '//creativecommons.org/licenses/by-nc-sa/4.0/',
'source': 'xenocanto',
'local_time': '12:49',
'detected_events': [[0.832, 2.48],
[2.992, 4.016],
[3.2, 3.904],
[5.472, 6.048],
[5.488, 6.432],
[7.088, 8.16],
[8.944, 10.432],
[10.72, 12.672],
[11.152, 13.2],
[13.488, 14.0],
[14.64, 16.496]],
'event_cluster': [1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
'quality': 'A'}
EXAMPLE TEST_5S
{'audio': {'path': '.ogg',
'array': array([-5.03219722e-04, 9.99580720e-04, 2.58744985e-05, ...,
-4.06746846e-03, -3.79991997e-03, 2.88472045e-04]),
'sampling_rate': 32000},
'filepath': '.ogg',
'start_time': 0.0,
'end_time': 5.0,
'low_freq': 2678,
'high_freq': 6053,
'ebird_code': None,
'ebird_code_multiclass': [0],
'call_type': None,
'sex': None,
'lat': 19.801668,
'long': -155.609444,
'microphone': 'Soundscape',
'license': 'Creative Commons Attribution 4.0 International Public License',
'source': 'https://zenodo.org/record/7078499',
'local_time': '15:00:06',
'detected_events': None,
'event_cluster': None,
'quality': None}
### Citation Information
```
@article{gadme,
author = {Rauch, Lukas and
Schwinger, Raphael and
Wirth, Moritz and
Heinrich, René and
Lange, Jonas and
Kahl, Stefan and
Sick, Bernhard and
Tomforde, Sven and
Scholz, Christoph},
title = {GADME: A Benchmark Towards General Avian Diversity Monitoring Evaluation in Deep Bioacoustics,
journal = {CoRR},
volume = {X},
year = {2024},
url = {X},
archivePrefix = {arXiv},
}
Note that each test in GADME dataset has its own citation. Please see the source to see
the correct citation for each contained dataset. Each file in the training dataset also has its own recordist. The licenses can be found in the metadata.
``` |