The benefits of full-spectrum data for
analyzing bat echolocation calls.
Full-spectrum (with SonoBat call trending) and zero-crossing interpretations
of the same Myotis californicus call signal in the presence of noise.
The most commonly used methods for processing bat echolocation calls,
full-spectrum analysis and zero-crossing, provide different interpretations of
acoustic signal content. Understanding how these two approaches extract
information content from acoustic data can help users interpret results and
select how and when to use each system.
Chris Corben developed the zero-crossing
system that became known as Anabat to
brilliantly enable the analysis of bat
echolocation calls in the day when
computers used floppy disks and simply
could not handle the enormous data load
of ultrasound. The digital points needed to
fully represent sound increases with
higher frequencies (more vibrations per time). A few seconds of bat
ultrasound recording would fill a floppy disk. Chris adapted zero-crossing to
rapidly extract and distill ultrasound data down to about a thousandth of the
digital points otherwise needed for full resolution and enabled widespread
investigation into the realm of bat echolocation.
Field investigations of bat echolocation supported by zero-crossing have
tremendously expanded our understanding of bat ecology and activity, but
have also revealed the flexibility and intraspecific variety of bat calls and the
challenges to readily identify bat species by their calls.
While zero-crossing extracts the basic time-frequency content of a signal,
full-spectrum adds the dimension of amplitude changes within bat calls, and
in contrast to zero-crossing that only retains the dominant frequency at any
time, full-spectrum data retains simultaneous multiple frequency content of a
signal at any time to interpret the full acoustic soundscape.
Indeed, the time-amplitude information from full-spectrum data does
enhance species discrimination. But perhaps the greater benefit of fullspectrum data comes from the higher quality and higher resolution timefrequency analysis it provides compared with that from zero-crossing and
this further enhances confident species identification. In addition, the full
soundscape information from full-spectrum provides metrics for quality
control essential for automated analysis.
Bat echolocation call data begins with vibrations of the bat’s larynx
imparting pressure waves on the air (i.e., sound) that propagate through the
air at high velocity (i.e., the speed of sound).
A sensor at a fixed point will oscillate
sympathetically as these waves pass.
Plot of pressure oscillation in
air per time measured from a
fixed point.
Plot of voltage oscillation in
detector circuitry per time
acquired from microphone.
Sound waveform
The strength, or amplitude, and the period of the oscillations
of the acquired electrical signal corresponds to the amplitude
and frequency of the original acoustic signal.
Both zero-crossing and full-spectrum analysis
begin with this same electrical signal data.
Zero-crossing analysis
Divide by 8– count every 8 period oscillations
Then plot those avg.
frequencies per time.
Measure the time to make every 8 oscillations; this
corresponds to the average frequency over the interval
of those 8 oscillations.
Zero-crossing analysis
Strong or weak signals with
the same frequency content
would plot equivalently.
Period counting is independent of signal strength.
Multiple frequency content and
zero-crossing analysis
Multiple signal sources contribute to real world soundscapes,
e.g., cascading water, vehicles on a roadway, wind blowing
past vegetation or structures, insects, and perhaps bats.
Pressure oscillations from multiple sources interact and
combine to form a single signal.
This example soundscape has two high
frequency sources from bats (blue and
green), and a stronger lower frequency
In the combined signal received by the
microphone, the stronger lower
frequency signal overpowers the higher
frequency signals and controls the zero
axis crossing, rendering the bat signals
undetectable by zero-crossing.
Multiple frequency content and
zero-crossing analysis
Zero-crossing analysis can only
detect the dominant, i.e.,
strongest, frequency content of any
signal. Any other signals in the
soundscape remain invisible to
zero-crossing analysis.
Full-spectrum analysis can access
multiple frequency content to reveal
the entirety of bat calls even when
the signal strength of all or part of
the a call falls below other signals in
the soundscape. Full-spectrum
analysis would reveal the bats in
this signal.
Full-spectrum analysis
Full-spectrum analysis by computer requires digital fullspectrum data, i.e., a digitized representation of the complete
acoustic waveform.
The highest frequency resolved by a digital representation of a
waveform depends upon how many digital samples of the waveform
were taken per time. Because a signal oscillates once up and once
down in each period, you basically need to sample at a rate of twice
that of the highest frequency desired (the Nyquist frequency). That
means 300,000 samples per second to resolve bat calls up to 150
kHz; much more data than required by zero-crossing– thank you to
Moore’s Law for making this practical.
Full-spectrum analysis
Full-spectrum analysis extracts frequency and amplitude
content by sampling overlapping snippets (windows) of
the waveform.
window sections
Full-spectrum analysis assembles these to construct a
representation of the entire soundscape by first passing the
signal through bandpass frequency filters and repeating this
process for each frequency band.
An example of a full-spectrum
rendered soundscape.
Full-spectrum analysis
A sonogram displays a flat plot of this type of data
with the amplitude mapped in color…
Full-spectrum analysis
A sonogram generated
from full-spectrum data
displays a high resolution
rendering of the time,
frequency, amplitude, and
multiple frequency content
of a signal.
Sonogram of two overlapping
Leptonycteris calls.
Full-spectrum & zero-crossing analysis compared
Sonogram of two overlapping Leptonycteris calls displayed beside the
same signal processed by zero-crossing (Z-C) divide by 8. Note how
Z-C can only track one signal at a time, and jumps between signals to
whichever has the most power in a time interval.
Full-spectrum & zero-crossing analysis compared
The scattered points at the beginning and end result from Z-C
interpretation of lower amplitude background noise, which Z-C cannot
readily discard because it cannot interpret relative amplitude of the
signals that generated these points.
Full-spectrum & zero-crossing analysis compared
Same signal with scattered
“noise” points removed.
In practice, zero-crossing users can specify the removal of extraneous
points to clean up the display. Software recognizes unwanted points by
distance from previous and next points in sequence.
Full-spectrum & zero-crossing analysis compared
Free-tailed bat, Tadarida brasiliensis
Full-spectrum processing
of a call with a SonoBat
generated timefrequency trend line
(yellow trace). The
overlain magenta points
display the zero-crossing
processed interpretation
of the same signal.
The zero-crossing processed time-frequency
trend jumps off the primary call signal as the call
amplitude diminishes and becomes overpowered
by the amplitude of the call’s echo.
Full-spectrum & zero-crossing analysis compared
With strong signals and no confounding additional signals
or noise, full-spectrum time-frequency trending and zerocrossing produce similar results.
Full-spectrum & zero-crossing analysis compared
Although too numerous to see individually at this scale, the fullspectrum processing of this call provided 238 points for
delineating the time-frequency trend line compared with 34 in the
trend from zero-crossing the same signal at divide by 8.
Full-spectrum processing provides higher resolution and higher
quality faithful renderings of the time-frequency domain of the calls.
Full-spectrum enables full-resolution time-frequency trends.
Full-spectrum & zero-crossing analysis compared
Because lower frequencies have fewer signal oscillations per time, zerocrossing will generate fewer trend points per time for lower frequency
signals. The strongest part of this Euderma maculatum call only
generated 5 trend points along the call’s fundamental with divide by 8.
Full-spectrum analysis will process all calls from the same detector and
with the same data format with equivalent resolution. Although the reduced
number of waves at lower frequencies also limits resolution, the overlapping windows of full-spectrum processing still provides more trend pts.
Full-spectrum & zero-crossing analysis compared
Reducing the division ratio improves zero-crossing call resolution
of low frequency signals. Divide by 4 reveals 13 trend points for
this Euderma maculatum call compared with 5 at divide by 8.
Full-spectrum & zero-crossing analysis compared
In the presence of multiple signals, zero-crossing processed calls may
jump off a call trend to a stronger signal. In this example, the trend line of
the higher frequency Parastrellus hesperus call nearly completes before
jumping down to the concurrent Eptesicus fuscus call.
This example had only minor effect upon rendering the P. hesperus call.
But note that that Z-C can not simultaneously reveal all of both calls. In
practice, calls recorded from open air foraging bats do not often overlap,
and other calls in the sequence will enable interpretation.
Full-spectrum & zero-crossing analysis compared
However, other signals can affect all of the calls within a sequence. Bats
vary amplitude through their calls. When the amplitude of competing
signals exceeds some parts of a recorded call, Z-C analysis will only
reveal the remaining portion(s) of the call that exceed the competing
signal amplitude, leaving call fragments.
Z-C processing of the
same signal.
The multiple frequency content available in full-spectrum data
enables tracking the time-frequency trends of calls to completion
even when the call amplitude falls below the maximum amplitude
of other concurrent signals.
Full-spectrum & zero-crossing analysis compared
Fully rendered time-frequency
trend using full-spectrum data
and processing.
Call fragment rendered
by Z-C processing of the
same signal.
Fully rendered time-frequency trends provide more confident and higher
quality extraction of call characteristics (e.g., characteristic frequency, Fc).
Higher quality data leads to better and more confident species discrimination.
Full-spectrum & zero-crossing analysis compared
Indiana bat (Myotis sodalis) call recorded in
the presence of audible insect sounds.
Z-C processing of the
same signal.
Full-spectrum processing rendered a complete time-frequency trend and
confident determination of call parameter data from this call despite insect
noise. Zero-crossing analysis of the same signal could not render a timefrequency trend because of the stronger low frequency insect signal.
Full-spectrum & zero-crossing analysis compared
These three Z-C pts correspond to
the strongest part of the call.
Z-C processing of the
same signal.
The overpowering concurrent signal amplitude that prevents full Z-C
recognition of bat time-frequency trends more seriously affects bats that
vocalize more quietly such as this Corynorhinus spp.
Full-spectrum & zero-crossing analysis compared
Lesser long-nosed bat, Leptonycteris yerbabuenae
first harmonic
second harmonic
first harmonic
Some bats shift power among their harmonics, and Z-C trending follows
these shifts generating interrupted time-frequency trends. Experienced Z-C
users can recognize these shifts and make assumptions about call
continuity, but they complicate automated analysis by Z-C. Full-spectrum
processing readily generates an uninterrupted trend with such data.
Full-spectrum & zero-crossing analysis compared
Townsend’s big-eared bat, Corynorhinus tonwnsendii
first harmonic
second harmonic
With access to multiple frequency content, full-spectrum processing can generate
uninterrupted time-frequency trends from which to determine call parameters with
greater confidence and detail for accurate species identification.
Full-spectrum & zero-crossing analysis compared
Fringed myotis, M. thysanodes
first harmonic
second harmonic
Fc ~ 40??
With some call types, Z-C trends that shift to stronger power in a harmonic
can obscure essential call details such as the characteristic frequency. This
can lead to ambiguous or misclassified identifications.
Full-spectrum & zero-crossing analysis compared
Fringed myotis, M. thysanodes
With some species, the call fragments acquired from out of range bats or
noise-burdened signals processed by Z-C, can leave fragments that mimic
the fully-formed calls of other species. The higher quality time-frequency call
trends supported by full-spectrum data minimize this source of error.
Full-spectrum & zero-crossing analysis compared
Eastern red bat, Lasiurus borealis
end of call
With access to the amplitude and multiple frequency content of full-spectrum
data, intelligent call trending algorithms, such as SonoBat’s shown in these
slides, can automatically track and interpret the end of calls through noise
and echoes that otherwise present ambiguous conditions for Z-C.
Full-spectrum & zero-crossing analysis compared
Long-eared myotis, M. evotis
Echoes from clutter often obscure ending details of calls. In this example, the
time-frequency trend processed from full-spectrum data revealed the downward
ending frequency trend that assists in recognition of this as a Myotis spp. call.
Full-spectrum & zero-crossing analysis compared
Free-tailed bat, Tadarida brasiliensis
Lasiurus cinereus??
Another example of a full-spectrum enabled time-frequency trend rendered
through clutter echoes. In this case, the ending downward trend in frequency
readily discriminates this from a hoary bat whose calls tend to turn upward
at the end, in contrast to free-tailed bats that tend to turn downward.
Full-spectrum & zero-crossing analysis compared
Fringed myotis, M. thysanodes
The amplitude and multiple frequency content of full-spectrum data
enables assessment of signal quality. For example, one such measure,
the signal to noise ratio (SNR), measures the relative strength of a
signal of interest (the call) to the strength of the background signal level.
noise level
signal level
Calls with low SNRs may generate unreliable parameters and are best
excluded from contributing to sequence-level species classifications. This
measure would reject the call in the above example. Such metrics provide
essential quality control for automated call and sequence classification.
Full-spectrum & zero-crossing analysis compared
Indiana bat, M. sodalis
Full sequence of an Indiana bat pass in the presence of audible insect noise
recorded and processed with full-spectrum data. Although lower in amplitude
than the lower frequency insect noise, full-spectrum processing still reveals
the bat calls.
Full-spectrum & zero-crossing analysis compared
Indiana bat, M. sodalis
Same sequence after applying a 25 kHz high pass filter. The multiple
frequency content of full-spectrum data enables post-recording
enhancement or removal of specific frequency bands to emphasize bat
signals and optimize parameter extraction for species identification.
Full-spectrum & zero-crossing analysis compared
Indiana bat, M. sodalis
Same full sequence of an Indiana bat pass recorded in the presence of
audible insect noise processed with zero-crossing. Because the amplitude of
the noise exceeded the bat calls, zero-crossing could reveal only some
fragments of a few bat calls.
Full-spectrum & zero-crossing analysis compared
Indiana bat, M. sodalis
Each zero-crossing point represents the highest amplitude frequency in the
time period over which that point averages. Zero-crossing data has no other
frequency content to emphasize or extract in post-recording processing.
The benefits of recording and
analyzing bat echolocation calls
using full-spectrum data extend
well beyond simply just assessing
the frequency of maximum power
or viewing call harmonics.
Although call parameters in the time-amplitude domain do increase
species classification performance, the primary benefit of full-spectrum
analysis involves increasing the robustness, accuracy, and confidence of
metrics traditionally used to describe bat calls with zero-crossing
methodology. In summary: full-spectrum provides higher quality results.
Of further benefit, the full-spectrum approach enables and supports
automated processing and classification of bat echolocation sequences
in which the enhanced information content of full-spectrum data
compensates for and decreases the need for human attention and
artistry in the interpretation of less information-rich data.
Joseph M. Szewczak, 2010