This page gathers all resources made available by BirdVox to the research community:
1. Companion websites for publications
These pages combine all resources associated to each of our papers: latest version of the manuscript, poster or slides when applicable, source code to reproduce experiments, and data.
- Salamon et al. PLOS ONE 2016. Towards the automatic classification of avian flight calls for bioacoustic monitoring.
- Salamon et al. ICASSP 2017. Fusing Shallow and Deep Learning for Bioacoustic Bird Species Classification.
- Lostanlen et al. ICASSP 2018. BirdVox-full-night: A Dataset and Benchmark for Avian Flight Call Detection.
- Lostanlen et al. SPL 2019. Per-channel energy normalization: Why and how.
- Lostanlen et al. PLOS ONE 2019. Robust sound event detection in bioacoustic sensor networks.
- Lostanlen et al. DCASE 2019. Long-distance Detection of Bioacoustic Events with Per-Channel Energy Normalization.
- Oudyk et al. VIHAR 2019. Matching Human Vocal Imitations to Birdsong: An Exploratory Analysis.
- Cramer et al. ICASSP 2020. Chirping Up the Right Tree: Incorporating Biological Taxonomies into Deep Bioacoustic Classifiers.
- Lostanlen et al. ICASSP 2020. Learning the Helix Topology of Musical Pitch.
2. Datasets
All datasets released by BirdVox are released under Creative Commons Internation Attribution License (CC-BY 4.0).
- CLO-43SD: a dataset for multi-class species identification in avian flight calls. 5,428 labeled audio clips of flight calls from 43 different species of North American woodwarblers (in the family Parulidae). The clips came from a variety of recording conditions, including clean recordings obtained using highly-directional shotgun microphones, recordings obtained from noisier field recordings using omnidirectional microphones, and recordings obtained from birds in captivity. Please cite our PLOS ONE 2016 paper when using this dataset for research.
- CLO-WTSP: a dataset for species-specific flight call identification for the White-Throated Sparrow. 16,703 labeled audio clips captured by remote acoustic sensors deployed in Ithaca, NY and NYC over the fall 2014 and spring 2015 migration seasons. Each clip is labeled to indicate whether it contains a flight call from the target species White-Throated Sparrow (WTSP), a flight call from a non-target species, or no flight call at all. Please cite our PLOS ONE 2016 paper when using this dataset for research.
- CLO-SWTH: a dataset for species-specific flight call identification for the Swainson’s Thrush. 179,111 labeled audio clips captured by remote acoustic sensors deployed in Ithaca, NY and NYC over the fall 2014 and spring 2015 migration seasons. Each clip is labeled to indicate whether it contains a flight call from the target species Swainson’s Thrush (SWTH), a flight call from a non-target species, or no flight call at all. Please cite our PLOS ONE 2016 paper when using this dataset for research.
- BirdVox-full-night: a dataset for species-agnostic avian flight call detection in continuous recordings. 62 hours of continuous audio from 6 sensors, with 35402 flight calls annotated in time and frequency. 5.7GB in FLAC format + 6 CSV tables for metadata. Please cite our ICASSP 2018 paper when using this dataset for research.
- BirdVox-70k: a dataset for species-agnostic flight call detection. A derivative work of BirdVox full-night, containing 70804 audio clips of duration 500 ms. Half of the clips are positive (contain one flight call at the center of the clip), the other half are negative (containing background noise or a non-flight-call acoustic event). 1.26GB in HDF5 format, containing both data and annotations. Please cite our ICASSP 2018 paper when using this dataset for research.
- BirdVox-DCASE-20k: a dataset for bird audio detection in 10-second clips. A derivative work of BirdVox-full-night, containing almost as much data but formatted into ten-second excerpts rather than ten-hour full night recordings. Out of the 20,000 recordings, 10,017 (50.09%) contain at least one vocalization (either song, call, or chatter) from a bird (not necessarily passerines). In addition, the BirdVox-DCASE-20k dataset is provided as a development set in the context of the “Bird Audio Detection” challenge, organized by DCASE (Detection and Classification of Acoustic Scenes and Events) and the IEEE Signal Processing Society. 17.6GB in WAV format + 1 CSV table for metadata. Please cite our ICASSP 2018 paper when using this dataset for research.
- BirdVox-ANAFCC and BirdVox-14SD: a cross-collection dataset for multi-class species identification in avian flight calls. An aggregate of different sources: BirdVox-70k, CLO-43SD, CLO-SWTH, CLO-WTSP, the Macaulay Library, Xeno-Canto and Old Bird. Please cite our ICASSP 2020 paper (Cramer et al.) when using this dataset for research.
3. Open-Source Software
- BirdVoxDetect: A deep learning system for detecting bird flight calls in continuous recordings.
- BirdVoxClassify: A deep learning system for classifying bird flight calls in audio clips.
- BirdVoxPaint: False-color spectrograms for long-duration bioacoustic monitoring.
- Kymatio: Scattering transforms in Python.
