Deciphering the dawn chorus
Supplementary content information
Find out how EPSRC supported research at Queen Mary University of London could provide more insight into how birds communicate with each other.
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The feature begins with the sound of a chiff chaff bird with short commentary by Dr Dan Stowell:
The timing is really important: it's not just some notes, but some notes with a particular sequencing. It's about a two hundred, three hundred milliseconds gap between each of them.
Dr Dan Stowell is a research fellow in machine listening at Queen Mary University of London. His work has already been used to develop an app called 'Warblr', which identifies a UK bird from the recording a user makes. Now he hopes to take the computer analysis of the sounds birds make to a new level, to discover more about the social interaction that is going on.
Traditionally you would take explicit measures, such as like how long is this sound, what frequency is that sound. But in order to go beyond that we use modern machine-learning methods, where you don't necessarily know how a computer has made a decision about a particular sound, but by training it, which means showing it lots of previous examples, we can encourage a computer algorithm to generalise from those.
Sound of zebra finches
At the University's laboratory aviary female zebra finches provide plenty of audio examples for Dan's research.
We've put the timing of the calls together with acoustic analysis of 'what's the content of that call?' Is it a short call or a long call for example? So with the zebra finches that we're working with, to some extent there's knowledge about what the calls are and what their purpose is. So when the birds are just hanging around together they very often make short calls to each other just in the ordinary course of business. So they just sound a bit like... (Dan imitates the sound of a zebra finch). If one of them gets separated a little bit, it doesn't have to be too far, maybe it gets separated a couple of metres from its partner, then it would do a distance call which sounds more like (Dan makes a slightly longer more emphatic sound) a little bit longer, a little bit more emphasis. It's quite clear from the content that it's for re-establishing contact and making sure that you've not lost your partner or your group.
We're starting by taking small, at this point, small groups of birds and recording all of the calls and use the timing of those calls to decipher – is this bird, when it calls, influencing another bird, are its calls causing another bird to call? It's very difficult to tell that just by listening to the recording, but if we apply an analysis which says 'does the probability of one bird calling increase after this bird calls or does it decrease or is there some more subtle interaction?' Then we can work out how strongly each bird influences each other and that gives us a kind of picture of the communication network in that group of birds.
All of Dan's research has been supported by the Engineering and Physical Sciences Research Council (EPSRC). In the longer term it could be used in a wide variety of areas:
Sound of a dawn chorus can be heard
Deciphering the dawn chorus is certainly one of the long-term goals of this kind of work, certainly something I'm very interested in.
The dawn chorus sound is re-established
The general application of automatic bird detection or automatic monitoring has a lot of significance in terms of monitoring populations and we know for example that bird populations, the latitude that they migrate to, depends at least in part on the effects of climate change, and so monitoring these things is important. Looking at the detail of bird vocalisations and how birds interact with each other is important in the long term for understanding animal communication, which includes human communication. People working on these things are looking at birdsong, at least in part, because it's an analogy to human language; songbirds learn to sing in an analogous fashion as humans learn to speak a language, and so we can improve the monitoring of animal sounds, we can improve the understanding, sort of decoding of animal sounds. More generally we actually have quite a lot of applications in which machines are going to need to understand the world around them through sound as well as through vision. Whether that's self-driving cars, whether that's your mobile phone, whether it's monitoring stations, monitoring CCTV for example. Although people have been working on speech recognition and speech technology for a long time, what this work can feed in to is a more general understanding, a more general sound analysis of an ordinary sound environment.
There was also an unforeseen but very welcome addition to Dan's research, which has come from the thousands of sounds collected by the Great British Public through the 'Warblr' app. This Big Data/ Citizen Science aspect will contribute to the machine learning work, to help a computer analyse whether a particular sound is or isn't made by a bird.
Sounds of the recordings of people imitating birds are played
One thing that we didn't quite expect was that people would like to test the recognition quality for themselves by making funny noises into the phone (laughs) and seeing what decision it came up with. So, as a result we have an unexpected extra benefit of this collection of bird impressions and whistling and squawking children and other things.
Examples of people imitating birds are played
Part of what's motivating this is essentially the big question - what is birdsong? You know, how can a computer know this is birdsong, this is a squeaky door, this is a small child? Those are the questions you have to try and really address if we're going to be able to automate this kind of detection. There are people creating projects right now where they have unattended microphone systems in the forest, recording and trying to identify which birds occur where. In order to be able to do that in any sort of scalable way we're going to need algorithms that can say- 'yes that is a bird' or 'no that's just a tree creaking in the wind.'