📏 Spectrum Measures

Note

This example shows the interactive capabilities of Matplotlib backends, and this will not appear in the static documentation. Please run this code on your machine to see the interactivity.

In this tutorial you will learn how to measure data from spectrums. Although there are many tools to get spectrum measure we are interested in select data from spectrum. Since we are interested into analyze birdsongs, we want to measure the maximum and mimum frequency of the theme and trill, number of trills, trill rate, trill and theme length, etc.

Google Colab Execution

If you are using Google Colabinstall the pacakge via pypi with the following code:

! git clone https://github.com/wavesongs/wavesongs
! pip install wavesongs
# enable hird party widgets (ipympl)
from google.colab import output
output.enable_custom_widget_manager()

In addition, you have to change the results and audio folder to:

audios = "/content/wavesongs/assets/audio"
results = "/content/wavesongs/assets/audio/results"

This is because in Google Colab all is downloaded at the contents folder.

Caution

If you encounter an error message, reset the kernel and run the above cell again. After that, everything should work successfully. Ensure that the audios and results variables are correctly set to the appropriate paths in your environment.

Libraries

Import the required classes and functions from wavesongs:

%matplotlib ipympl

from wavesongs import plot
from wavesongs.objs.song import Song
from wavesongs.utils.paths import ProjDirs
from wavesongs.utils.tools import get_measures

Project Directory

audios = "./assets/audios"
results = "./assets/results"

proj_dirs = ProjDirs(audios=audios, results=results)

Define Song

# Region of Interest
tlim_roi = (0.7, 2.7)

copeton_song = Song(proj_dirs, file_id="574179401", tlim=tlim_roi)
copeton_song.acoustical_features(umbral_FF=1.4, NN=256, ff_method="yin", flim=(1e2, 2e4))

plot.spectrogram_waveform(copeton_song, tlim=tlim_roi, save=False)

copeton_song.play()

Select Data

klicker_data = plot.spectrogram_data(copeton_song)

You will have a similar image as below.

Data measures example

Measures Data Frame

df_measures = get_measures(klicker_data, copeton_song, save=False)
df_measures

	fmax	fmin	theme_trill_time_sep	trill_bw	trill_rates	trill_len_times	trill_len_freqs	trill_slopes	trill_tinis	trill_tends	...	theme_tinis	theme_tends	theme_types	themes_avg_len_time	themes_band_width	theme_sep_freq_means	theme_sep_time_means	theme_sep_freqs	theme_sep_times	no_themes
0	8847.391083	2511.227834	-0.471877	5677.202875	18.700922	[np.float64(0.050308468411643714), np.float64(...	[np.float64(5523.764959048709), np.float64(562...	[np.float64(-109797.91540961032), np.float64(-...	[np.float64(1.9849188047711863), np.float64(2....	[np.float64(8661.690974019351), np.float64(877...	...	[np.float64(0.8823403248272411), np.float64(1....	[np.float64(5470.989898989899), np.float64(558...	['down', 'down']	0.510553	2483.266605	2005.715335	0.011908	[np.float64(2005.7153351698807)]	[np.float64(0.011907939580554583)]	2

1 rows × 33 columns