I'm a senior researcher in machine listening - which means using computation to understand sound signals. Currently my research is all about bird sounds, but I have also worked on voice, music and environmental soundscapes.
I am an EPSRC research fellow based at Queen Mary University of London, giving me five years to research "structured machine listening for soundscapes with multiple birds". I am developing automatic processes to analyse large amounts of sound recordings - 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.
- D. Stowell, E. Benetos, and L. F. Gill, On-bird sound recordings: Automatic acoustic recognition of activities and contexts. IEEE/ACM Trans. on Audio Speech and Language Processing, 25(6), 1193-1206, 2017.
- D. Stowell. Computational Bioacoustic Scene Analysis. In Computational Analysis of Sound Scenes and Events, T. Virtanen, M. D. Plumbley, and D. P. W. Ellis (eds.), Springer, Oct. 2017.
- E. Benetos, D. Stowell, and M. D. Plumbley. Approaches to complex sound scene analysis. In Computational Analysis of Sound Scenes and Events, T. Virtanen, M. D. Plumbley, and D. P. W. Ellis (eds.), Springer, Oct. 2017.
- H. Pamula et al, Adaptation of deep learning methods to nocturnal bird audio monitoring, in LXIV Open Seminar on Acoustics (OSA) 2017, Piekary Śląskie, Poland. 2017.
- D. Stowell, L. F. Gill, and D. Clayton. Detailed temporal structure of communication networks in groups of songbirds. Journal of the Royal Society Interface, 13(119), 2016.
- D. Stowell, V. Morfi, and L. F. Gill. Individual identity in songbirds: signal representations and metric learning for locating the information in complex corvid calls. In Proceedings of InterSpeech 2016. 2016.
- D. Stowell, M. Wood, Y. Stylianou, and H. Glotin. Bird detection in audio: a survey and a challenge. In Proceedings of MLSP 2016. 2016.
- P. A. Alvarado and D. Stowell. Gaussian processes for music audio modelling and content analysis. In Proceedings of MLSP 2016. 2016.
- D. Stowell and D. Clayton, Acoustic event detection for multiple overlapping similar sources. Proceedings of IEEE WASPAA, 2015.
- D. Stowell and M. D. Plumbley, Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning. PeerJ 2:e488, 2014.
- D. Stowell and M. D. Plumbley, Segregating event streams and noise with a Markov renewal process model. Journal of Machine Learning Research 14, 1891-1916, 2013.
- D. Stowell, D. Giannoulis, E. Benetos, M. Lagrange and M. D. Plumbley, Detection and Classification of Audio Scenes and Events. IEEE Transactions on Multimedia 17(10), 1733-1746, 2015.
- D. Stowell and M. D. Plumbley, Large-scale analysis of frequency modulation in birdsong databases. Methods in Ecology and Evolution, 2014.
Full publication listing on my QMUL homepage.
- Co-chair of ICEI 2018 session on "Analysis of ecoacoustic recordings: detection, segmentation and classification"
- Chair of IBAC 2017 session on "Machine Learning Methods in Bioacoustics"
- Chair of EUSIPCO 2017 special session on "Bird Audio Signal Processing"
- Lead organiser of the Bird Audio Detection challenge
In the media
Science: "Computer becomes bird enthusiast"
RTE Radio 1: Conversation about automatic birdsong identification on The Mooney Show: MP3 link
- Veronica Morfi: "Machine transcription of wildlife bird sound scenes"
- Pablo Alvarado Duran: "Physically and Musically Inspired Probabilistic Models for Audio Content Analysis"
- Will Wilkinson: "Sound effect synthesis"
I'm pleased to be working with some great people across different research fields. This includes researchers in my home group the Centre for Digital Music, plus QMUL zoologist colleagues including David Clayton lab and Alan McElligott.
Why does it matter?
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.