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BirdSet

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Audio Classification
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bird classification
passive acoustic monitoring
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metadata
task_categories:
  - audio-classification
license: cc
tags:
  - bird classification
  - passive acoustic monitoring

Dataset Description

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) 5,460 10,296 12,000 5.92 21
NBP (nips) 24,327 5,493 563 29.9 51
NES (columbia_costa_rica) 16,117 6,952 24,480 14.2 89
PER (amazon_basin) 16,802 14,798 15,120 10.5 132
POW (powdermill_nature) 14,911 16,052 4,560 15.7 48
SNE (sierra_nevada) 19,390 20,147 23,756 20.8 56
SSW (sapsucker_woods) 28,403 50,760 205,200 35.2 81
UHH (hawaiian_islands) 3,626 59,583 36,637 4.92 25 tr, 27 te
XCM (xenocanto) 89,798 x x 89.3 409
XCL (xenocanto) 528,434 x x 484 9,734

FEATURES 

{
    "audio": datasets.Audio(sampling_rate=32_000, mono=True, decode=True),
    "filepath": datasets.Value("string"),
    "start_time": datasets.Value("float64"),  # can be changed to timestamp later
    "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=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")),
    "peaks": datasets.Sequence(datasets.Value("float64")),
    "quality": datasets.Value("string"),
    "recordist": datasets.Value("string"),
        })

EXAMPLE TRAIN
{'audio': {'path': '.ogg',
  'array': array([ 0.0008485 ,  0.00128899, -0.00317163, ...,  0.00228528,
          0.00270796, -0.00120562]),
  'sampling_rate': 32000},
 'filepath': '.ogg',
 'start_time': None,
 'end_time': None,
 'low_freq': None,
 'high_freq': None,
 'ebird_code': 0,
 'ebird_code_multilabel': [0],
 'ebird_code_secondary': ['plaant1', 'blfnun1', 'butwoo1', 'whtdov', 'undtin1', 'gryhaw3'],
 'call_type': 'song',
 'sex': 'uncertain',
 'lat': -16.0538,
 'long': -49.604,
 'length': 46,
 'microphone': 'focal',
 'license': '//creativecommons.org/licenses/by-nc-sa/4.0/',
 'source': 'xenocanto',
 'local_time': '18:37',
 'detected_events': [[0.736, 1.824],
  [9.936, 10.944],
  [13.872, 15.552],
  [19.552, 20.752],
  [24.816, 25.968],
  [26.528, 32.16],
  [36.112, 37.808],
  [37.792, 38.88],
  [40.048, 40.8],
  [44.432, 45.616]],
 'event_cluster': [0, 0, 0, 0, 0, -1, 0, 0, -1, 0],
 'peaks': [14.76479119037789, 41.16993396760847],
 'quality': 'A',
 'recordist': '...'}

EXAMPLE TEST_5S
{'audio': {'path': '.ogg',
  'array': array([-0.67190468, -0.9638235 , -0.99569213, ..., -0.01262935,
         -0.01533066, -0.0141047 ]),
  'sampling_rate': 32000},
 'filepath': '.ogg',
 'start_time': 0.0,
 'end_time': 5.0,
 'low_freq': 0,
 'high_freq': 3098,
 'ebird_code': None,
 'ebird_code_multilabel': [],
 'ebird_code_secondary': None,
 'call_type': None,
 'sex': None,
 'lat': 5.59,
 'long': -75.85,
 'length': None,
 'microphone': 'Soundscape',
 'license': 'Creative Commons Attribution 4.0 International Public License',
 'source': 'https://zenodo.org/record/7525349',
 'local_time': '4:30:29',
 'detected_events': None,
 'event_cluster': None,
 'peaks': None,
 'quality': None,
 'recordist': 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. ```