Cacophony Blog

27 Aug 2018 - 12:20
Internals of the new hardware

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.

New battery packs

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.

30 Jul 2018 - 11:09
I am Dr. Ayesha Hakim, a computer scientist with an aim to use my skills to do something 'Good for Nature'. I joined Cacophony to contribute towards preserving New Zealand's birds in the wild. My research interests include behaviour analysis using machine learning techniques and producing pretty graphics to visualise the complex data. To be specific, I am interested in recognition and behaviour analysis of humans and birds using audio and visual signals.

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

11 Jul 2018 - 11:57

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.

29 Jun 2018 - 10:34

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.

15 Jun 2018 - 15:38

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.

13 Jun 2018 - 12:33

You have to love the way journalists make a headline. They get most of the details of the actual project pretty right though

14 May 2018 - 13:27

Below is a talk from the recent New Zealand AI conference. It gives an up-to-date summary of the project with particular reference to how we are using Machine Learning.

4 May 2018 - 14:19
A critical component of our Cacophonator devices is the FLIR Lepton 3 thermal camera. It's our eyes in the dark as we look for invasive predators. As discussed previously, reliably extracting video frames out of the camera has been a challenge. Our software would often "lose sync" with the camera, resulting in lost video data.
13 Apr 2018 - 11:08

As with all projects when the fun development parts are proven there is refinement required to make the devices more robust and usable. Below are a few photos of the next iteration of our hardware that makes the product more reliable and flexible.

4 Apr 2018 - 10:57

The Cacophony Project's Menno Finlay-Smits recently recorded an episode with Dave Lane for the Access Granted podcast. It's a great introduction to the what the project is all about and where we're heading.

16 Mar 2018 - 09:48

We have been invited to present our work at New Zealand's AI conference. There is currently huge interest in exploring applications for AI around the world and in New Zealand. The Cacophony Project is a great example of how this field can be used to tackle a very New Zealand specific problem.

To book your tickets to the event click here.

22 Feb 2018 - 11:16

The first step required to become 100% predator-free is to know for sure what type of predators are out there. The most common methods used to do this are tracking tunnels and chew cards. These tools require significant manual work and miss a lot of predators. Tracking cameras do a much better job of tracking predators and detect 2-10 times more than tracking tunnels.

17 Feb 2018 - 12:06

Latest version of the Cacophonometer can be downloaded from link below.  In this version:

12 Feb 2018 - 11:24

With the generous help of Willowbank Wildlife Reserve we have been collecting thermal video footage of kiwis to help train our machine learning based animal classifier.

Here's a sample of the recordings we've been collecting...

27 Jan 2018 - 08:22

The Machine Learning (artificial intelligence) identification has two main parts. A tracker locates any objects of interest and separates them from the background. These tracks are then passed to the classifier which analyses them to identify what sort of animal they contain.

Previously we used a static camera so the tracking part of the problem was quite easy as only the animals moved, whereas background objects (such as trees) stayed relatively still. This is obviously harder when the camera is moving. Below shows some examples of the new tracker working with the moving camera.

19 Jan 2018 - 12:56

In previous blog post we have talked about the advantages of having a camera that can track animals. It will obviously allow better identification but also the ability to eliminate with something like a poison paintball.

The video below shows the latest experiments. We are just using a radio controlled car for testing but you will get the idea. It is a little amusing to see the artificial intelligence try to guess what the radio controlled car is.

15 Jan 2018 - 14:51


We now have many thermal cameras deployed at various locations collecting recordings every night. This means we have a lot of thermal video footage to manually tag every day so that they can be used to improve our machine learning classifier. As I write this, we have collected almost 30,000 thermal video recordings.

Our recordings include many false positives - where a non-animal object triggered our camera's motion detector. We also have many, many recordings of birds - particularly at dawn and dusk. Filtering through all the false positives and bird footage is very time consuming.

22 Dec 2017 - 11:35

This is just a quick peek at some very encouraging results showing that Artificial Intelligence can successfully work out what it is seeing from the video. We will put up more discussion and details next year. 

The raw video is on the left. On the right, the animal is identified and the cumulative classification of the animal is at the top. The instantaneous guess from the Artificial Intelligence is changing in real time at the bottom. 

Video 1: Example of classification of animals using AI

1 Dec 2017 - 17:34

Hi everyone, my name is Matthew, I have been brought on to help with the machine learning side of things.  I’m very excited to be part of this project.

My job here is to take all the thermal footage we have been recording and identify the animals in it.

29 Nov 2017 - 13:25

It has been shown that trail cameras are much better at detecting predators compared to tracking tunnels, which are the default detection tool. Trail cameras are between 2-10 times more sensitive at detecting predators than tracking tunnels depending on species. This is obviously very important as we need to be able to measure all predators that are out there. 

20 Nov 2017 - 11:16

Hi everyone, I’m Finn. I’ve been involved with Cacophony for a year now. I started out on work based around the Cacophonometers from a hardware and business model angle. Most recently I’ve been working on software and the development of a method to analyse all of this birdsong we’re getting! This post will describe the work I’ve done and basically what The Cacophony Index 1.0 is.

27 Oct 2017 - 12:12
If you're keen to help rid New Zealand of invasive predators and are a software developer based in Christchurch, you might want to take a look at this opportunity.
26 Oct 2017 - 11:09

In previous work we thought we had the ultimate predator detection camera. But our goal is to detect all predators so we have chosen to develop a higher resolution heat camera. Here's a reminder of why we want to detect all predators:

2 Oct 2017 - 11:25

It's been a little longer than usual since our last update. We've been busy!

18 Sep 2017 - 11:47

We achieved some fantastic milestones last week!

11 Sep 2017 - 13:54

Here's another look into what we've been up to recently.

1 Sep 2017 - 15:29

It's been a busy week for the Cacophony Project, with good progress on many fronts.

Project Manager

As noted earlier, the project now has a new lead developer & project manager (me!) and I've spent much of the week getting up to speed. I've been reviewing the current state of our hardware and software efforts, meeting various advisors and contributors, and getting a grasp of our immediate and longer term goals. It's been fantastic to meet everyone and I'm really excited to be onboard. There's a real energy around the project.

28 Aug 2017 - 10:30

My name is Menno Finlay-Smits and I start today as the Lead Developer and Project Manager for the Cacophony Project. We've got a real chance at solving the problem of invasive predators in New Zealand and I'm really excited to be part of the effort.

27 Jul 2017 - 16:29

July update - now that's sounding like I might do regular updates...

What have I been doing?