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AviaNZ birdsong analysis software

AviaNZ version 1.1 is released! The program is easy to use and equipped with the essentials for spectrogram reading, analysis, manual annotation, and also few other stimulating options to the next stage of the development: automated analysis of your field recordings. You can download the software along with the user manual following the links below. We thank to those of you who shared your experence while using the program with us enabling to improve it. We welcome your feedback and it is very important to us as we continue developing AviaNZ.

 AviaNZ  

Download User Manual

Last update: 16 Sep 2018

 
 
Windows  Download AviaNZ v1.1
Mac & Linux Download code
  Instructions

Marsden Fund for AviaNZ

Stephen Marsland and Isabel Castro received Marsden Fund in this latest round to work on their project  AviaNZ: Making Sure New Zealand Birds Are Heard.

Stephen Marsland and Isabel Castro with Blandy the kiwi

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Sponsor a kiwi

To track and study our kiwi, they each need individual radio-transmitters and we need radio-telemetry equipment, batteries, and cameras to be able to follow them. In total, we spend $470 per kiwi each year and we would like to ask you to sponsor this cost for one or more of our birds.

Sponsor a kiwi

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Birdsong Denoising Using Wavelets

Automatic recording of birdsong is becoming the preferred way to monitor and quantify bird populations worldwide. Programmable recorders allow recordings to be obtained at all times of day and year for extended periods of time.Consequently, there is a critical need for robust automated birdsong recognition. One prominent obstacle to achieving this is low signal to noise ratio in unattended recordings. Field recordings are often very noisy: birdsong is only one component in a recording, which also includes noise from the environment (such as wind and rain), other animals (including insects), and human-related activities, as well as noise from the recorder itself. We describe a method of denoising using a combination of the wavelet packet decomposition and band-pass or low-pass filtering, and present experiments that demonstrate an order of magnitude improvement in noise reduction over natural noisy bird recordings.

Featured Research

Automated birdsong recognition in complex acoustic environments: a review
Priyadarshani, N. , Marsland, S. and Castro, I. (2018), Automated birdsong recognition in complex acoustic environments: a review. J Avian Biol, 49: jav-01447. doi:10.1111/jav.01447 
 
The impact of environmental factors in birdsong acquisition using automated recorders
Priyadarshani N, Castro I, Marsland S. (2018), The impact of environmental factors in birdsong acquisition using automated recorders. Ecol Evol. 2018;8:5016–5033. https://doi.org/10.1002/ece3.3889
 
Birdsong Denoising Using Wavelets
Priyadarshani N, Marsland S, Castro I, Punchihewa A (2016) Birdsong Denoising Using Wavelets. PLoS ONE 11(1): e0146790. https://doi.org/10.1371/journal.pone.0146790
Denoised examples and source code
 
Experimental test of birdcall detection by autonomous recorder units and by human observers using broadcast
Isabel Castro, Alberto De Rosa, Nirosha Priyashardani, Leanne Bradbury and Stephen Marsland (2018), Experimental test of birdcall detection by autonomous recorder units and by human observers using broadcast. Ecology and Evolution [accepted for publication]