Functions for working with files created by EvTAF and the evsonganaly GUI.
In case you need to work with those files in Python 😊😊😊 (see "Usage" below).
The first work published with data collected using EvTAF and evsonganaly is in this paper:
Tumer, Evren C., and Michael S. Brainard.
"Performance variability enables adaptive plasticity of ‘crystallized’adult birdsong."
Nature 450.7173 (2007): 1240.
https://www.nature.com/articles/nature06390
These functions are translations to Python of the original functions written in MATLAB (copyright Mathworks) by Evren Tumer (shown below).
$ pip install evfuncs
The main purpose for developing these functions in Python was to work with files of Bengalese finch song in this data repository: https://figshare.com/articles/Bengalese_Finch_song_repository/4805749
Using evfuncs with that repository, you can load the .cbin audio files ...
>>> import evfuncs
>>> rawsong, samp_freq = evfuncs.load_cbin('gy6or6_baseline_230312_0808.138.cbin')... and the annotation in the .not.mat files ...
>>> notmat_dict = evfuncs.load_notmat('gy6or6_baseline_230312_0808.138.cbin')(or, using the .not.mat filename directly)
>>> notmat_dict = evfuncs.load_notmat('gy6or6_baseline_230312_0808.138.not.mat')...and you should be able to reproduce the segmentation of the raw audio files of birdsong into syllables and silent periods, using the segmenting parameters from a .not.mat file and the simple algorithm applied by the SegmentNotes.m function.
>>> smooth = evfuncs.smooth_data(rawsong, samp_freq)
>>> threshold = notmat_dict['threshold']
>>> min_syl_dur = notmat_dict['min_dur'] / 1000
>>> min_silent_dur = notmat_dict['min_int'] / 1000
>>> onsets, offsets = evfuncs.segment_song(smooth, samp_freq, threshold, min_syl_dur, min_silent_dur)
>>> import numpy as np
>>> np.allclose(onsets, notmat_dict['onsets'])
True(Note that this test would return False if the onsets and offsets in the .not.mat
annotation file had been modified, e.g., a user of the evsonganaly GUI had edited them,
after they were originally computed by the SegmentNotes.m function.)
evfuncs is used to load annotations by
'crowsetta',
a data-munging tool for building datasets of vocalizations
that can be used to train machine learning models.
Two machine learning libraries that can use those datasets are:
hybrid-vocal-classifier,
and vak.
Please feel free to raise an issue here:
https://github.com/NickleDave/evfuncs/issues
If you use this package, please cite the DOI: