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AviaNZ birdsong analysis software

AviaNZ version 1.5.1 is released with some improvemets and bug fixes! You can download the software along with the user manual following the links below. 

The program is equipped with the essentials for spectrogram reading, analysis, and manual annotation. AviaNZ offers advanced features such as denoising, automated analysis of your field recordings, and the options to review the results in a user friendly manner. In this version, we also include the facility to train a filter to detect any target apecies calls in field recordings and this module is under development and testing.   

Thanks 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.

Who's in our forests?

AviaNZ is among the sfTI (Science for Technological Innovation) challenge SEED projects. 

Marsdan

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Bird counting computers

Listen to the Radio New Zealand interview with Stephen Marsland here

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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AviaNZ  Download User Manual 
Last update: 09 Aug 2019 
   
Windows Version Mac & Linux Version
Download AviaNZ v1.5.1 Download code

Instructions

Instructions

Featured Research

AVIANZ: A future‐proofed program for annotation and recognition of animal sounds in long‐time field recordings
Marsland, S, Priyadarshani, N, Juodakis, J, Castro, I. (2019), AVIANZ: A future‐proofed program for annotation and recognition of animal sounds in long‐time field recordings. Methods Ecol Evol. 2019; 00: 1– 7; doi:10.1111/2041-210X.13213 [pdf] [data]
 
Experimental test of birdcall detection by autonomous recorder units and by human observers using broadcast
Castro, I., Alberto, D. R., Priyadarshani, N., Bradbury, L. and Marsland, S. (2019), Experimental test of birdcall detection by autonomous recorder units and by human observers using broadcast. Ecol Evol. 2019;00:1–22. https://doi.org/10.1002/ece3.4775 Ecol Evol. 2019;00:1–22; doi:10.1002/ece3.4775
 
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; doi: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): e0146790doi:10.1371/journal.pone.0146790
Denoised examples and source code