Conventional trail cameras are cheap and offer high image resolutions. They are designed for detecting larger animals such as pigs and deer. Thermal cameras, like those used by the Cacophony Project, are much more expensive and typically have lower resolution. Conventional cameras need an IR light source to "see" at night while thermal cameras do not.
Some of the predator monitoring and trapping solutions we are developing require more power than traditional tools. This article explores why we think the effectiveness of tools is more important than power use for most of the applications we are interested in.
We're really excited about 2040, a social venture that is commercialising the technologies developed by the Cacophony Project. They've recently published a blog article about some ongoing research by Tim Hunt from Wintec who is using audio recordings gathered by the Bird Monitor product to automatically detect and identify morepork calls.
We have created a new tool that allows us to test many different methods to lure and capture predators of any type, using sound and thermal vision. We are calling this tool the Cacophony Predator Lab.
In this blog article, we will discuss how sound lure experiments can be set up to test the effectiveness of various sounds for attracting possums. The sound lure software is configured from a web interface that allows you to upload any set of sounds and play them at any volume or time sequence.
Author: Ben McEwen
Hi, I’m Ben. I am a student in my final year of Mechatronics Engineering at the University of Canterbury. This semester I have been working with the Cacophony Project to explore improved ways of tracking animals in video footage for better predator recognition and for the elimination of invasive predator species.
Recent testing of the Cacophony Project's thermal camera has reinforced how good this type of technology is for predator monitoring and control. This blog post highlights the core reasons that the thermal camera developed by The Cacophony Project is a great technology for this application.
The key advantages are:
Authors: Grant Ryan, James Ross, Elaine Murphy and Merel Jansen
As highlighted in earlier blog posts, the Cacophony Thermal camera is much more sensitive for the detection of predators than the next best tool. It is able to see 3 to 50 times more activity, allows us to make observations that have never been possible before.
Last week, Menno Finlay-Smits from the Cacophony Project participated on a Techweek 2019 panel which was centred on the idea of "Tech for Good". The panelists each represented an organisations that each attempt to "do good" in the world in its own way. We discussed various ways that organisations can do this, as well as emerging entity structures that focus on more than just profit.
We have continued the rat detection experiments recently described on this blog and have updated results to report.
Authors: David Blake and Grant Ryan
David Blake is a contributor to the Cacophony Project and has been running an experiment to test the relative sensitivity of Cacophony Project thermal cameras to off-the-shelf trail cameras for detection of rats. It is well
Every year there are a number of compelling reports of encounters with the elusive South Island Kōkako. This precious bird with a beautiful haunting song was once declared extinct but hope remains of finding it alive and bringing it back from the brink of extinction.
The Cacophony Project is fortunate to have many talented people helping to move it forward.
The Cacophony Project is an ambitious project with many moving parts. It can be difficult to understand what our technology does and how the various components of the project fit together.
Here's the high level parts of the project and how they relate to each other:
We are often asked the status of the project so we thought it would be useful to share a table which highlights how we think about our progress. These are a set of milestones that need to be achieved to make New Zealand predator free. We define various phases that each milestone moves through as progress is made.
On November 4 2018, the Cacophony Project was privileged to become a signatory of the Banks Peninsula 2050 Predator Free Initiative. Along with key personnel from DOC, Ecan, Christchurch City Council, Banks Peninsula Iwi and Banks Peninsula Conservation Trust, the Cacophony Project's Clare McClennan was present to participate in the signing ceremony held at the Living Springs amphitheater. This historic partnership aims to focus and coordinate efforts to eliminate invasive predators on Banks Peninsula.
This article comes to us from Tim Armitage who has recently installed a Cacophonometer at his property to help monitor the effects of predator controls.
Early October 2018 we received our Cacophonometer at our Sandspit home. The setup process was very simple with the app having been pre-installed and the main next steps being getting our account created online and the device registered. The location we chose was around 20 meters from our house (a site with power available) and a small WiFi extender soon fulfilled the coverage required to ensure the upload process was reliable. We soon had plenty of recordings to sample with the ‘meter following the pattern set by the software – i.e. greater intensity of records around dawn and dusk.
The thermal video footage from the Cacophonator devices has proven to be invaluable for machine learning and studying predator behaviour. Occasionally, however, we noticed that recording for some animals was starting later than it should. In this article we discuss the reasons why this was happening and what we have done to improve our animal detection algorithm.
David Blake is a semi-retired property investor who now likes to spend his time trapping pests and planting native trees. From time to time he helps out by volunteering at The Cacophony Project doing some filming and occasionally contributing to parts of the software.
The video in this link shows a visual representation of the contributions to the software components of the project over time. The visualisation is based on source code changes pushed to Github and shows the areas of the software being worked on and the people involved. You can see how the project builds momentum as more and more clever people get involved. It also highlights the sophistication of the software that links all the parts of our predator-free projects together.
The Cacophony Project started with the development of a simple cell phone based tool that can measure birdsong so there is an objective measure of how well birds are doing as predator control is rolled out. This product is now ready to be more widely tested around New Zealand.
The last couple of months have seen us make more improvements to our Cacophonator hardware. The changes made have been driven by the demands of upcoming projects and targets.
Up until now we've typically run our devices on mains power. We've preferred sites which are near native bush but have access to a wall socket (sometimes with long extension cords!). This has gotten us quite far in terms of testing our prototypes and gathering footage to train our machine learning classifier but obviously isn't going to be a long term solution. Being able to run on battery power opens up a huge range of new areas to our devices.
The Cacophonator hardware now incorporates a buck-boost converter which allows it to work from a number of types of power sources including various battery technologies with differing output voltages (which change as the battery discharges). The buck-boost converter also continues to support mains power using a classic "wall wart" AC adapter.
We explored a number of options for battery power and after a number of false starts and experiments we've found a New Zealand based manufacturer who will make weatherproof lithium-ion battery packs which meet our needs exactly. These packs have performed well in the cold and in heavy rain. A Cacophonator can run for 5-6 nights (turning off during the day) on a single battery pack and we have ideas on how to extend battery life further.
Acoustic recordings of birds have been used by conservationists and ecologists to determine population density of specific bird species in a region. However, it is very hard to analyse and visualise the presence/absence of a specific bird species by manually hearing these recordings even by an expert bird song specialist. I am working on developing computational tools to automatically classify and visualise bird sounds in order to recognise different bird species in the wild. It is a powerful combination of machine learning, ecology, and applications of multimedia visualisation. These tools can be used by conversationalists, ornithologists, ecologists, and evolutionary scientists to visually identify a bird’s species using their sound alone.
In this blog, I want to share some of the initial results obtained by automatic clustering of bird species based on their sound features using machine learning techniques. It is an attempt to find similarities between sounds of the same bird species and with other bird species as well as differentiating between birds and human sounds. Many thanks to Nirosha Priyadarshani, Stephen Marsland, Isabel Castro, and Amal Punchihewa for making their datasets available for further research. These datasets are available at https://github.com/smarsland/AviaNZ.
The Cacophony Project has very long-term goals to enable us to eliminate 100% of predators. It’s easy to look at what we are doing and assume that this will never work in the depths of the remote bush. While we acknowledge that there are many steps before we achieve that capability, the tools developed while getting to that end goal will be useful for other important parts of the problem.
Hi I’m Clare and I’m the newest member of the core Cacophony Project team.
I’m a passionate outdoor adventurer, native flora and fauna lover and an experienced software developer. I love the interesting and diverse nature of the Cacophony Project and find it exciting to be able to work on a project aligns perfectly with my values and interests.
At first look it seems like it should be easy to work out how effective traps are. Our first way of measuring it was very simple - how often do we see animals around a trap compared to how often they are caught by the trap. This is too simplistic: with enough time an animal will eventually wander into a trap and be caught. Any device with some chance of killing/luring will have a 100% success rate given infinite time. Therefore, time should be one of the parameters for a device’s effectiveness.