README.md · DBD-research-group/BirdSet at df79dfdc15e2a55b66b613adee71665123d2eaac

metadata

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

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	80,012	x		83	409
XCL (xenocanto)	492,676	x		485	9,734

FEATURES

{
  "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")
        })

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}

EXAMPLE TEST
{'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': 6.8,
 'end_time': 8.2,
 'low_freq': 2678,
 'high_freq': 6053,
 'ebird_code': 22,
 'ebird_code_multiclass': None,
 '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.