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Three New Datasets For Bioacoustic Machine Learning

23/11/2016

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We're happy to announce the release of 3 new datasets for research on automatic bioacoustic bird species recognition. The datasets were compiled for our recently published study "Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring", and are freely available on the Dryad Digital Repository:
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  • CLO-43SD: 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.
Picture
Rosetta Stone For Warblers’ Migration Calls. Source: https://www.allaboutbirds.org/a-rosetta-stone-for-identifying-warblers-migration-calls/
  • CLO-WTSP: 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.​
  • CLO-SWTH: 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.
​
CLO-43SD is targeted at the closed-set N-class problem (identify which of of these 43 known species produced the flight call in this clip), while CLO-WTSP and CLO-SWTH are targeted at the binary open-set problem (given a clip determine whether it contains a flight call from the target species or not). The latter two come pre-sorted into two subsets: Fall 2014 and Spring 2015. In our study we used the fall subset for training and the spring subset for testing, simulating adversarial yet realistic conditions that require a high level of model generalization.

For further details about the datasets see our article:

​
Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring
J. Salamon , J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck and S. Kelling
PLOS ONE 11(11): e0166866, 2016. doi: 10.1371/journal.pone.0166866. 
[PLOS ONE][PDF][BibTeX]

You can download all 3 datasets from the Dryad Digital Repository at this link.
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Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring

23/11/2016

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PictureA white-throated sparrow, one of the species targeted in the study. Image by Simon Pierre Barrette, license CC-BY-SA 3.0.
Automatic classification of animal vocalizations has great potential to enhance the monitoring of species movements and behaviors. This is particularly true for monitoring nocturnal bird migration, where automated classification of migrants’ flight calls could yield new biological insights and conservation applications for birds that vocalize during migration. In this paper we investigate the automatic classification of bird species from flight calls, and in particular the relationship between two different problem formulations commonly found in the literature: classifying a short clip containing one of a fixed set of known species (N-class problem) and the continuous monitoring problem, the latter of which is relevant to migration monitoring. We implemented a state-of-the-art audio classification model based on unsupervised feature learning and evaluated it on three novel datasets, one for studying the N-class problem including over 5000 flight calls from 43 different species, and two realistic datasets for studying the monitoring scenario comprising hundreds of thousands of audio clips that were compiled by means of remote acoustic sensors deployed in the field during two migration seasons. We show that the model achieves high accuracy when classifying a clip to one of N known species, even for a large number of species. In contrast, the model does not perform as well in the continuous monitoring case. Through a detailed error analysis (that included full expert review of false positives and negatives) we show the model is confounded by varying background noise conditions and previously unseen vocalizations. We also show that the model needs to be parameterized and benchmarked differently for the continuous monitoring scenario. Finally, we show that despite the reduced performance, given the right conditions the model can still characterize the migration pattern of a specific species. The paper concludes with directions for future research.

The full article is available freely (open access) on PLOS ONE:


​Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring
J. Salamon , J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck and S. Kelling
PLOS ONE 11(11): e0166866, 2016. doi: 10.1371/journal.pone.0166866. 
[PLOS ONE][PDF][BibTeX]

Along with this study, we have also published the three new datasets for bioacoustic machine learning that were compiled for this study.

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