Editing Bat detector (section)

==Other bat detector types==

===Zero crossing analysis===

ZCA is most commonly associated with the Anabat bat detector from Titley Scientific.<ref>{{Cite web|title=FAQ's - Support|url=https://www.titley-scientific.com/us/support/faqs|access-date=2020-06-29|website=www.titley-scientific.com}}</ref> The original bat calls are digitised and the zero crossing points used to produce a data stream which is recorded on a memory card. There are sophisticated timing and trigger controls and the device can be set to respond to bat calls, so that many hours of recording are available in unmanned situations. The purpose of ZCA is to reduce the amount of data which must be recorded to the memory and may be considered as a simple form of [[lossy compression|lossy data compression]]. Historically, to achieve long recording times, such information reduction has been necessary due to memory capacity limitations and memory cost.

The solid state ZCA recording is analysed by custom software to produce a time/frequency plot of each call which can be examined for species recognition in a similar way to FD or TE recordings.

====How it is used====

The ZCA detector is usually placed in a bat roost or bat flight path and left for a number of days to collect data. Thus it is less labour-intensive than using a manned bat detector in real time.

====Pros and cons====

While the ZCA detector can also be used in real time, its value is for remote recording over long periods. The analysis is similar to that for FD recordings, but there is no amplitude data included. However it does accurately record each zero crossing point, rather than only one in ten. As with all recording devices triggered by an input, a ZCA detector recording automatically is prone to ultrasonic interference from insects such as crickets. Filters can be written to select a characteristic frequency of certain species and ignore others; some (CF species) are more easily filtered, others are nigh on impossible.

===High frequency recording===

This can be done by using a high speed digitiser peripheral on a computer such as a laptop. This is not a bat detector as such, but recordings of bat calls can be analysed similarly to TE recordings. This method produces large data files and produces no means of detecting bat calls without the simultaneous use of a bat detector. There are however also more sophisticated systems such as the Avisoft-UltraSoundGate that can replace a conventional bat detector. These advanced systems additionally provide a real-time spectrographic display, automated call parameter measurement and classification tools, integrated GPS functionality and a versatile metadata input tool for documenting the recordings.

===DSP detectors===

DSP bat detectors aim to provide an acoustically accurate portrayal of bat calls by using a [[digital signal processor]] to map bats' ultrasounds signals to audible sounds; different algorithms are being used to accomplish this, and there is active development and tuning of algorithms going on.

One strategy called "frequency shifting" uses a FFT signal analysis in order to find the main frequency and signal power, then using digital simulation a new audible wave is synthesized from the original one divided by a defined value.

The processes of Frequency Division and Heterodyne conversion can also be performed digitally.

===Time domain signal coding===

This type of bat detector is believed to be in pre-production or experimental and is not available commercially.{{Citation needed|date=April 2011}} Research is in progress for analysing many types of ultrasound calls and sounds besides those of bats.<ref name="TDSC_Paper">{{cite journal|last1=Chesmore|first1=E. D|title=Application of time domain signal coding and artificial neural networks to passive acoustical identification of animals|journal=Applied Acoustics|date=1 December 2001|volume=62|issue=12|pages=1359–1374|doi=10.1016/S0003-682X(01)00009-3|language=en}}</ref>

A TDSC detector digitises the original calls and derives a two dimensional data string by analysing the parameters of each call with respect to time. This is analysed by a [[Artificial neural network|neural network]] to provide pattern recognition for each species.