Photo of Dan

Email: dstowell [aatt] tilburguniversity.edu
Twitter: @mclduk
Blog: here

Dan Stowell

Associate Professor of AI & Biodiversity

Recent news

I am an Associate Professor of AI & Biodiversity working in the Netherlands (Tilburg University, Naturalis Biodiversity Centre, JADS). My research is about machine listening and computational bioacoustics - which means using computation (especially machine learning) to understand animal sounds and other sound signals.

I develop automatic processes to analyse large amounts of sound recordings - for example 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. The research work is focussed on the machine learning and signal processing methods that can help with these questions. I also work with others to apply these methods to biodiversity monitoring.

I am also a Fellow of the Alan Turing Institute. I work with OpenClimateFix and OpenStreetMap on addressing climate change through solar panel mapping.

I use various machine learning methods, including Gaussian processes, point process models, feature learning... sure, yes, and deep learning too, lots of deep learning.

Selected publications

Full publication listing on my Google Scholar profile, plus preprints on arXiv.

Selected chairing/organising

Datasets

All are published under open data licences:

In the media

Nature: interviewed in March 2019 for article "AI empowers conservation biology"

Climate Home News: "One million solar panels! If only we knew where they were"

Science: "Computer becomes bird enthusiast"

BBC: "Software can decode bird songs"

BBC Radio 4: Featured on The World Tonight talking about automatic bird identification and Warblr

RTE Radio 1: Conversation about automatic birdsong identification on The Mooney Show: MP3 link

PhD students

Past:

Why does it matter?

What's the point of analysing animal sounds? And why do it with computational methods? 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 collaborate regularly 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), and similar patterns are being recorded worldwide. We have great organisations such as the BTO and the RSPB (in the UK) or Sovon (in the Netherlands), 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.