March 14, 2016 Code C ARU ABMI bioacoustics
Automated acoustic monitoring is gaining momentum worldwide. Alberta is stepping up to the game by implementing automated recording unit (ARU) based monitoring programs. An improved command line tool is here to help in the process.
The Bioacoustic Unit of the Alberta Biodiversity Monitoring Institute (ABMI) and the Bayne lab at the University of Alberta collaborates on best practices for using acoustic technology. The amount of information collected each year by these organizations is measured in dozens of terabytes, and is steadily increasing. Efficient and secure storage for all these files is the most immediate challenge, but the next one is closing the gap between data collection and data processing.
Processing all the recordings from the field
requires significant computing resources.
The first step is converting the wac files to wav, so that a a wider
variety of software tools can be used to analyze the information in the files.
The wac format is a proprietary file format developed by
Wildlife Acoustics,
a company that specializes in bioacoustics monitoring systems.
The fact that the acoustic units manufactured by Wildlife Acoustics
are widely used in Alberta might represent a vendor lock-in.
Luckily for us, the pressure on the company
(see here and here, thanks Luis J. Villanueva-Rivera)
led to the company releasing a command line tool under the GPL license
for facilitating
wac-to-wav file conversion (see source code here, here, and here).
The story might have ended right there. But the C code worked with
standard input and output. It took some time and help (thanks John) to figure out exactly
how one should use the command line tool. Here is the solution:
cat input_file.wac | ./wac2wavcmd > output_file.wav
Isn’t that ugly? One would expect something like:
./wac2wavcmd input_file.wac output_file.wav
The good news is that the modified version (also released under GPL license) does just that.
It has been tested on Linux, Mac OS X and Windows 10.
It also removes all the clutter the original program prints to the
terminal. The only difference is that the program is called wac2wav
instead of wac2wawcmd. See the description and source code on
GitHub.
(Note: the leading ./ can be omitted if the program is added to the path.)
We are one step closer to a truly cloud based bioacoustic platform!
I moved to Canada in 2008 to start a postdoctoral fellowship with Prof. Subhash Lele at the stats department of the University of Alberta. Subhash at the time just published a paper about a statistical technique called data cloning. Data cloning is a way to use Bayesian MCMC algorithms to do frequentist inference. Yes, you read that right.
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