Tag: Warblr

A sound social venture: recognising birds

Dan Stowell was a researcher at Queen Mary University of London when he founded an early version of what is now known as a Social Venture: a company created to do social good. With Florence Wilkinson, he turned birdsong into a tech-based social good.

A Eurasian Wren singing on the end of a branch
A Eurasian Wren: Image by Siegfried Poepperl from Pixabay

His research is about designing methods that computers can use to make sense of bird sounds. One day he met Florence Wilkinson, who works with businesses and young people, and they discovered they both had the same idea: “What if we could make an app that recognises bird sounds?” They decided to create a startup company, Warblr, to make it happen. However, unlike many research driven startups its main aim was not to make money but to do a social good. Dan and FLorence built this into their company mission statement:

…to reconnect people with the natural world through technology. We want to get as many people outdoors as possible, learning about the wildlife on their doorstep and how to protect it.

Dan brought the technical computer science skills needed to create the app, and Florence brought the marketing and communication skills needed to ensure people would hear about it. Together, they persuaded Queen Mary University of London’s innovation unit to give them a start-up grant. As a result their app Warblr exists and even gained some press coverage.

It can help people connect with nature by helping recognise birds – after all one of the problems with bird watching is they are so damned hard to spot and lots that flit by just look like little brown things! However, they are far easier to hear. Once you know what is out there then it adds incentive to try to actually spot it. However, the app has another purpose too. It collects data about the birds spotted, recording the species and where and when it was seen, with that data then made freely available to researchers.

Social ventures are a relatively new idea that universities are now supporting to help their researchers do social good that is sustainable and not just something that lasts until the grants run out. As Dan and Florence showed though, as a researcher you do not need to commit to do everything. To be a successful innovator you need more than technical skills, though. You need the ability to be part of a great team and to recognise a sound deal!

Updated from the archive, written by Paul Curzon, Queen Mary University of London.

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What’s that bird? Ask your phone – birdsong-recognition apps

by Dan Stowell, Queen Mary University of London

Robin image by Darren Coleshill from Pixabay

Could your smartphone automatically tell you what species of bird is singing outside your window? If so how?

Mobile phones contain microphones to pick up your voice. That means they should be able to pick up the sound of birds singing too, right? And maybe even decide which bird is which?

Smartphone apps exist that promise to do just this. They record a sound, analyse it, and tell you which species of bird they think it is most likely to be. But a smartphone doesn’t have the sophisticated brain that we have, evolved over millions of years to understand the world around us. A smartphone has to be programmed by someone to do everything it does. So if you had to program an app to recognise bird sounds, how would you do it? There are two very different ways computer scientists have devised to do this kind of decision making and they are used by researchers for all sorts of applications from diagnosing medical problems to recognising suspicious behaviour in CCTV images. Both ways are used by phone apps to recognise bird song that you can already buy.

The sound of the European robin (Erithacus rubecula) better known as robin redbreast, Recorded by
Vladimir Yu. Arkhipov, Arkhivov CC BY-SA 3.0 via wikimedia

Write down all the rules

Blackbird singing
Blackbird Image by Ian Lindsay from Pixabay

If you ask a birdwatcher how to identify a blackbird’s sound, they will tell you specific rules. “It’s high-pitched, not low-pitched.” “It lasts a few seconds and then there’s a silent gap before it does it again.” “It’s twittery and complex, not just a single note.” So if we wrote down all those rules in a recipe for the machine to follow, each rule a little program that could say “Yes, I’m true for that sound”, an app combining them could decide when a sound matches all the rules and when it doesn’t.

This is called an ‘expert system’ approach. One difficulty is that it can take a lot of time and effort to actually write down enough rules for enough birds: there are hundreds of bird species in the UK alone! Each would need lots of rules to be hand crafted. It also needs lots of input from bird experts to get the rules exactly right. Even then it’s not always possible for people to put into words what makes a sound special. Could you write down exactly what makes you recognise your friends’ voices, and what makes them different from everyone else’s? Probably not! However, this approach can be good because you know exactly what reasons the computer is using when it makes decisions.

The sound of a European blackbird (Turdus merula) singing merrily in Finland, from Wikipedia (song 1). Public Domain via wikimedia

This is very different from the other approach which is…

Show it lots of examples

A lot of modern systems use the idea of ‘machine learning’, which means that instead of writing rules down, we create a system that can somehow ‘learn’ what the correct answer should be. We just give it lots of different examples to learn from, telling it what each one is. Once it has seen enough examples to get it right often enough, we let it loose on things we don’t know in advance. This approach is inspired by how the brain works. We know that brains are good at learning, so why not do what they do!

One difficulty with this is that you can’t always be sure how the machine comes up with its decisions. Often the software is a ‘black box’ that gives you an answer but doesn’t tell you what justifies that answer. Is it really listening to the same aspects of the sound as we do? How would we know?

On the other hand, perhaps that’s the great thing about this approach: a computer might be able to give you the right answer without you having to tell it exactly how to do that!

It means we don’t need to write down a ‘recipe’ for every sound we want to detect. If it can learn from examples, and get the answer right when it hears new examples, isn’t that all we need?

Which way is best?

There are hundreds of bird species that you might hear in the UK alone, and many more in tropical countries. Human experts take many years to learn which sound means which bird. It’s a difficult thing to do!

So which approach should your smartphone use if you want it to help identify birds around you? You can find phone apps that use one approach or another. It’s very hard to measure exactly which approach is best, because the conditions change so much. Which one works best when there’s noisy traffic in the background? Which one works best when lots of birds sing together? Which one works best if the bird is singing in a different ‘dialect’ from the examples we used when we created the system?

One way to answer the question is to provide phone apps to people and to see which apps they find most useful. So companies and researchers are creating apps using the ways they hope will work best. The market may well then make the decision. How would you decide?

This article was originally published on the CS4FN website and can also be found on pages 10 and 11 of Issue 21 of the CS4FN magazine ‘Computing sounds wild’. You can download a free PDF copy of the magazine (below), or any of our other free material at our downloads site.

Further bird- (& computing-) themed reading
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What are birds actually saying?

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EPSRC supports this blog through research grant EP/W033615/1.