Today we launched Warblr, our app for automatically recognising the sounds of the UK's hundreds of bird species.
It's Â£3.99, and it's in the Apple Store here.
It's built using our research here at QMUL. The research was funded by the EPSRC - they funded me to do the basic research, and they also funded the "innovation" grant that helped turn it into software people can use on their phone.
One question you might wonder... If it's based on public research funding, why is it a paid app? We're going with a spin-out model, creating a business (a social enterprise with open data and conservation goals) and we believe that's a good route to making it sustainable. The basic research is publicly available to all.
I'm particularly happy to see the Guardian did a head-to-head test of our app and another one. Yes they agreed our app was better :) but the broader point is that this research on machine learning and sound is now reaching the point where, like speech recognition, it becomes more than just a research idea to become something people can use as an everyday tool.
The data we used during development: Xeno Canto, the big crowdsourced bird sound database, has been invaluable. And more recently the British Trust for Ornithology also very kindly allowed us to use some of their bird monitoring data (collected by thousands of volunteers over decades) as part of the recognition process.
The data we collect: we shall see! But a big motivation for this endeavour is to collect audio as well as geospatial data, that can help research and one day will also help organisations such as the BTO to monitor bird conservation.
It's been interesting getting to this point. Thanks to all who helped us on our way, including my business partner Florence Wilkinson who's been working tirelessly on this. And a personal thanks from me to Mark Plumbley for his enthusiastic support and discussion all through the early stages of this research!