| | --- |
| | task_categories: |
| | - audio-classification |
| | license: cc |
| | tags: |
| | - bird classification |
| | - passive acoustic monitoring |
| | --- |
| | ## Dataset Description |
| |
|
| | - **Repository:** [https://github.com/DBD-research-group/GADME](https://github.com/DBD-research-group/BirdSet) |
| | - **Paper:** [GADME](https://arxiv.org/abs/2403.10380) |
| | - **Point of Contact:** [Lukas Rauch](mailto:lukas.rauch@uni-kassel.de) |
| |
|
| | ### Datasets |
| | We present the BirdSet benchmark that covers a comprehensive range of classification datasets in avian bioacoustics. |
| | We offer a static set of evaluation datasets and a varied collection of training datasets, enabling the application of diverse methodologies. |
| |
|
| | We have a complementary code base (soon to be made public): https://github.com/DBD-research-group/BirdSet |
| | and a complementary paper (work in progress): https://arxiv.org/abs/2403.10380 |
| |
|
| |
|
| | | | train | test | test_5s | size (GB) | #classes | |
| | |--------------------------------|--------:|-----------:|--------:|-----------:|-------------:| |
| | | [PER][1] (Amazon Basin) | 16,802 | 14,798 | 15,120 | 10.5 | 132 | |
| | | [NES][2] (Colombia Costa Rica) | 16,117 | 6,952 | 24,480 | 14.2 | 89 | |
| | | [UHH][3] (Hawaiian Islands) | 3,626 | 59,583 | 36,637 | 4.92 | 25 tr, 27 te | |
| | | [HSN][4] (high_sierras) | 5,460 | 10,296 | 12,000 | 5.92 | 21 | |
| | | [NBP][5] (NIPS4BPlus) | 24,327 | 5,493 | 563 | 29.9 | 51 | |
| | | [POW][6] (Powdermill Nature) | 14,911 | 16,052 | 4,560 | 15.7 | 48 | |
| | | [SSW][7] (Sapsucker Woods) | 28,403 | 50,760 | 205,200| 35.2 | 81 | |
| | | [SNE][8] (Sierra Nevada) | 19,390 | 20,147 | 23,756 | 20.8 | 56 | |
| | | [XCM][9] (Xenocanto Subset M) | 89,798 | x | x | 89.3 | 409 | |
| | | [XCL][10](Xenocanto Complete) | 528,434| x | x | 484 | 9,734 | |
| |
|
| | [1]: https://zenodo.org/records/7079124 |
| | [2]: https://zenodo.org/records/7525349 |
| | [3]: https://zenodo.org/records/7078499 |
| | [4]: https://zenodo.org/records/7525805 |
| | [5]: https://github.com/fbravosanchez/NIPS4Bplus |
| | [6]: https://zenodo.org/records/4656848 |
| | [7]: https://zenodo.org/records/7018484 |
| | [8]: https://zenodo.org/records/7050014 |
| | [9]: https://xeno-canto.org/ |
| | [10]: https://xeno-canto.org |
| |
|
| | - We assemble a training dataset for each test dataset that is a subset of a complete Xeno-Canto (XC) snapshot. We extract all recordings that have vocalizations of the bird species appearing in the test dataset. |
| | - We use the .ogg format for every recording and a sampling rate of 32 kHz. |
| | - Each sample in the training dataset is a recording may have more than one vocalization of the corresponding bird species. |
| | - Each recording in the training datasets has a unique recordist and the corresponding license from XC. We omit all recordings from XC that are CC-ND. |
| | - The bird species are translated to ebird_codes |
| | - Snapshot date of XC: 03/10/2024 |
| | |
| | **Train** |
| | - Exclusively using focal audio data from 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 the corresponding test soundscape files. |
| | - We transform the scientific names of the birds into the corresponding ebird_code label. |
| | - 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_5s** |
| | - Task: Multilabel ("ebird_code_multilabel") |
| | - Only soundscape data from Zenodo formatted acoording to the Kaggle evaluation scheme. |
| | - Each recording is segmented into 5-second intervals where each ground truth bird vocalization is assigned to. |
| | - This contains segments without any labels which results in a [0] vector. |
| | |
| | **Test** |
| | - Task: Multiclass ("ebird_code") |
| | - 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"). |
| | |
| | ### Quick Use |
| | - For multi-label evaluation with a segment-based evaluation use the test_5s column for testing. |
| | - You could only load the first 5 seconds or a given event per recording to quickly create a training dataset. |
| | - We recommend to start with HSN. It is a medium size dataset with a low number of overlaps within a segment |
| | |
| | ### Metadata |
| | |
| | | | format | description | |
| | |------------------------|-------------------------------------------------------:|-------------------------:| |
| | | audio | Audio(sampling_rate=32_000, mono=True, decode=True) | audio object from hf | |
| | | filepath | Value("string") | relative path where the recording is stored | |
| | | start_time | Value("float64") | only testdata: start time of a vocalization in s | |
| | | end_time | Value("float64") | only testdata: end time of a vocalzation in s | |
| | | low_freq | Value("int64") | only testdata: low frequency bound for a vocalization in kHz | |
| | | high_freq | Value("int64") | only testdata: high frequency bound for a vocalization in kHz | |
| | | ebird_code | ClassLabel(names=class_list) | assigned species label | |
| | | ebird_code_secondary | Sequence(datasets.Value("string")) | only traindata: possible secondary species in a recording | |
| | | ebird_code_multilabel | Sequence(datasets.ClassLabel(names=class_list)) | assigned species label in a multilabel format | |
| | | call_type | Sequence(datasets.Value("string")) | only traindata: type of bird vocalization | |
| | | sex | Value("string") | only traindata: sex of bird species | |
| | | lat | Value("float64") | latitude of vocalization/recording in WGS84 | |
| | | long | Value("float64") | lontitude of vocalization/recording in WGS84 | |
| | | length | Value("int64") | length of the file in s | |
| | | microphone | Value("string") | soundscape or focal recording with the microphone string | |
| | | license | Value("string") | license of the recording | |
| | | source | Value("string") | source of the recording | |
| | | local_time | Value("string") | local time of the recording | |
| | | detected_events | Sequence(datasets.Sequence(datasets.Value("float64")))| only traindata: detected audio events in a recording with bambird, tuples of start/end time | |
| | | event_cluster | Sequence(datasets.Value("int64")) | only traindata: detected audio events assigned to a cluster with bambird | |
| | | peaks | Sequence(datasets.Value("float64")) | only traindata: peak event detected with scipy peak detection | |
| | | quality | Value("string") | only traindata: recording quality of the recording (A,B,C) | |
| | | recordist | Value("string") | only traindata: recordist of the recording | |
| | |
| | #### Example Metadata Train |
| | |
| | ```python |
| | {'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 Metadata Test5s |
| | |
| | ```python |
| | {'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': [1, 10], |
| | '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{birdset, |
| | 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 = {BirdSet: A Multi-Task Benchmark For Classification In Avian Bioacoustics}, |
| | journal = {CoRR}, |
| | volume = {X}, |
| | year = {2024}, |
| | url = {X}, |
| | archivePrefix = {arXiv}, |
| | } |
| | |
| | Note that each test subset in the BirdSet 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 noted. The licenses can be found in the metadata. |
| | ``` |
