Detecting illegal fishing in Tanzania

Introduction

In 2018, Gill and Jamie

However, during the analysis of the acoustic data it became apparent that there was a frequently detected loud “clapping” noise. After some investigation it became apparent that these were the long range acoustic signatures of illegal “blast fishing” – a fishing technique in which a bomb is thrown into the water to kill or stun fish, causing them to rise dead to the surface, and allowing them to be quickly and easily scooped up by fishermen.

The conservation implications of blast fishing include: indiscriminate killing of all species within the bomb’s range, damage to coral reefs, and significant noise pollution. We were looking for animals but discovered that our survey method also had the power to reveal how common illegal blast fishing was, and where it was happening. So we also produced another paper, this one focusing on the large number of bomb blasts that were detected. This got quite some traction in the press.

After the survey, other hydrophones detected the same thing: lots of bomb blasts. However, the acoustic detection of any bomb was still just opportunistic, as it was recorded in other projects which were not exclusively focused on addressing the conservation concern of blast fishing. It became clear that what was needed was a long term acoustic study which could locate the likely positions of each blast and quantify the full extent of the problem.

Detecting illegal fish blasts

And so, in 2018, Gill, Jamie and Code4Africa teamed up to do exactly that.

We deployed 4 state-of-the-art recording stations along the northern coast of Tanzania. The recording stations each have 3 synchronized hydrophones linked to a state of the art recording unit (SoundTraps), allowing us to work out a bearing to a received bomb blast. If 2 or more of the 4 stations picked up a blast, a latitude/longitude location can be determined. The recording devices were based on ultra low power acoustic devices and so can be deployed easily be a team of divers, something that’s really important where you don’t have access to specialized research vessels to deploy and recover gear.

How do we find bomb blasts on a noisy reef?

In total over a 2 year period we deployed three acoustic sensors contiously for a total of XXX days of acoustic recordings. How do we process that quantity of data on a very tight budget? Thankfully, there are some open-source projects out there to help us. The first stage was to process the acoustic data using PAMGuard, a program our lab actively contributes devleoper time to. PAMGuard is great at quickly churning through large datasets and picking out interesting sections. The sound a bomb blast makes is low in frequency and lasts for a significant period of time (a second or so), and so we used a simple energy detector to pick up likely candiates. This worked great, with thousands of short audio clips of potential bomb blasts generated. However there’s a bunch of low frequency sounds on reefs and many of them are not bombs. For example… Audio Player

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So the next stage was to determine how to find which clips contain actual bomb blasts? With the recent advances in machine learning, it might initially seem sensible to train a neural net or other type of classifier to find bomb blast (i.e. to manually find some bombs for training data, train a classifier, and run it on the rest of the data). However there are a few issues with this. A classifier is only as good as it’s training data. So, that training data would have to be manually identified to begin with, which could be time consuming. In addition, this is very novel data. What if noise conditions change? What if there’s a species that starts vocalising during a different period of the year that confuses the classifier? To be diligent with the data analysis, even once the classifier has been trained, a manual analyst would have to check the classifier performance, at least for the first few years, by which time the project might be over. Having a manual analyst listen to all the clips from PAMGuard is also not an option, as it is still far too time consuming on a tight budget.

The solution is to a take a machine-assisted approach. Rather than training a machine to make decisions, we created highly interactive tools combined with machine learning to allow a manual analyst to always have the final say. This cuts the time it take to analyse large data by an order of magnitude but maintains the valuable human oversight (we are, after all, still the best pattern recognition and decision making machines when it comes to bio-acoustics analysis!). We called the program SoundSort and it proved it extremly effective with around 2 months of acoustic data taking around 4 hours to process and manually validate.

Results

The project was a success, with bombs detected and located showing a general trend that bomb bladsts were decreasing. You can find the full report here and a few detected bomb blasts below.

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