We used custom programs (available at https://github.com/ornith) to detect, extract and time-stamp sound events, to identify and remove noise (e.g., wing-flapping or noise from degraded radio signal) and non-focals' vocalisations, to automatically define onset and offset of syllables, and to classify them via a k-means clustering paradigm (Ter Maat et al., 2014).
Other analyses used custom programs.
Data were analyzed using custom programs designed to display data in a web-based format [ 34].
Videos were exported from Volocity as TIFF files and assembled into stacks by time point using custom programs in MATLAB (MathWorks), and the ND-Safir binary was called repeatedly to process each time point.
Data were analyzed using custom programs written in MATLAB™ and in Python, using the numpy, scipy (www.scipy.org) and matplotlib packages (matplotlib.sourceforge.net).net
Signals from the brain were digitized and recorded using the Multi-Channel Neurophysiology Workstation Tucker-Daviss Technologies, FL, USA) and analyzed offline using custom programs in MATLAB.
To estimate parameters for demographic models described below, we used coalescent simulations to draw random ancestral recombination graphs (ARGs) conditional on specific demographic parameter values and compute the likelihood of observing the experimental data given these ARGs using custom programs.
Diffusion tensor images were processed using custom programs run on MATLAB software (version 7, The MathWorks, Natick, MA) that calculated the diffusion tensor eigenvalues in each voxel [33].
Analysis of the recordings was performed using custom programs in Signal 5 (Cambridge Electronic Design Limited, Cambridge, UK).
Images were processed offline using custom programs written in LabVIEW and MATLAB (Mathworks, Natick, MA, USA).
