10,092 research outputs found
ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning
Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species
Vaex: Big Data exploration in the era of Gaia
We present a new Python library called vaex, to handle extremely large
tabular datasets, such as astronomical catalogues like the Gaia catalogue,
N-body simulations or any other regular datasets which can be structured in
rows and columns. Fast computations of statistics on regular N-dimensional
grids allows analysis and visualization in the order of a billion rows per
second. We use streaming algorithms, memory mapped files and a zero memory copy
policy to allow exploration of datasets larger than memory, e.g. out-of-core
algorithms. Vaex allows arbitrary (mathematical) transformations using normal
Python expressions and (a subset of) numpy functions which are lazily evaluated
and computed when needed in small chunks, which avoids wasting of RAM. Boolean
expressions (which are also lazily evaluated) can be used to explore subsets of
the data, which we call selections. Vaex uses a similar DataFrame API as
Pandas, a very popular library, which helps migration from Pandas.
Visualization is one of the key points of vaex, and is done using binned
statistics in 1d (e.g. histogram), in 2d (e.g. 2d histograms with colormapping)
and 3d (using volume rendering). Vaex is split in in several packages:
vaex-core for the computational part, vaex-viz for visualization mostly based
on matplotlib, vaex-jupyter for visualization in the Jupyter notebook/lab based
in IPyWidgets, vaex-server for the (optional) client-server communication,
vaex-ui for the Qt based interface, vaex-hdf5 for hdf5 based memory mapped
storage, vaex-astro for astronomy related selections, transformations and
memory mapped (column based) fits storage. Vaex is open source and available
under MIT license on github, documentation and other information can be found
on the main website: https://vaex.io, https://docs.vaex.io or
https://github.com/maartenbreddels/vaexComment: 14 pages, 8 figures, Submitted to A&A, interactive version of Fig 4:
https://vaex.io/paper/fig
Geocoded data structures and their applications to Earth science investigations
A geocoded data structure is a means for digitally representing a geographically referenced map or image. The characteristics of representative cellular, linked, and hybrid geocoded data structures are reviewed. The data processing requirements of Earth science projects at the Goddard Space Flight Center and the basic tools of geographic data processing are described. Specific ways that new geocoded data structures can be used to adapt these tools to scientists' needs are presented. These include: expanding analysis and modeling capabilities; simplifying the merging of data sets from diverse sources; and saving computer storage space
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