I've been awarded a 5-year research fellowship! It's funded by the EPSRC and gives me five years to research "structured machine listening for soundscapes with multiple birds". What does that mean? It means I'm going to be developing computerised processes to analyse large amounts of sound recordings - automatically detecting the bird sounds in there and how they vary, how they relate to each other, how the birds' behaviour relates to the sounds they make.
What's the point of analysing bird sounds? Well...
One surprising fact about birdsong is that it has a lot in common with human language, even though it evolved separately. Many songbirds go through similar stages of vocal learning as we do, as they grow up. And each species is slightly different, which is useful for comparing and contrasting. So, biologists are keen to study songbird learning processes - not only to understand more about how human language evolved, but also to help understand more about social organisation in animal groups, and so on. I'm not a biologist but I'm going to be collaborating with some great people to help improve the automatic sound analysis in their toolkit - for example, by analysing much larger audio collections than they can possibly analyse by hand.
Bird population/migration monitoring is also important. UK farmland bird populations have declined by 50% since the 1970s, and woodland birds by 20% (source). We have great organisations such as the BTO and the RSPB, who organise professionals and amateurs to help monitor bird populations each year. If we can add improved automatic sound recognition to that, we can help add some more detail to this monitoring. For example, many birds are changing location year-on-year in response to climate change (source) - that's the kind of pattern you can detect better when you have more data and better analysis.
Sound is fascinating, and still surprisingly difficult to analyse. What is it that makes one sound similar to another sound? Why can't we search for sounds as easily as we can for words? There's still a lot that we haven't sorted out in our scientific and engineering understanding of audio. Shazam works well for music recordings, but don't be lulled into a false sense of security by that! There's still a long way to go in this research topic before computers can answer all of our questions about sounds.
I'll be developing automatic analysis techniques (signal processing and machine learning techniques), building on starting points such as my recent work on tracking multiple birds in an audio recording and on analysing frequency-modulation in bird sounds. I'll be based at Queen Mary University of London.
I'll also be collaborating with some experts in machine learning, in animal behaviour, in bioacoustics. One of the things on the schedule for this year is to record some zebra finches with the Clayton Lab. I've met the zebra finches already - they're jolly little things, and talkative too! :)