Bird Audio Detection Challenge 2016: public data

Data labels for the Bird Audio Challenge 2016. These annotations indicate the presence/absence of bird sounds in various datasets of 10-second audio clips.<br><br>More info:<br>http://machine-listening.eecs.qmul.ac.uk/bird-audio-detection-challenge/<br><br>===============<br><br>REUSE:<br><br>The data is published under a Creative Commons CC-BY licence, and so you may reuse it as long as you attribute the origin. In academic work, please do this via a citation to our paper on the challenge:<br><br>Dan Stowell, Mike Wood, Yannis Stylianou, Hervé Glotin. Bird detection in audio: a survey and a challenge, in <i>Proceedings of MLSP 2016</i>, arXiv:1608.03417 [cs.SD], 2016.<br><br>You may also wish to link directly to this dataset, which you can do via its DOI https://dx.doi.org/10.6084/m9.figshare.3851466<br><br>===============<br><br>How the annotations were collected:<br><br>The label "hasbird" represents whether a sound clip contains any audible bird sound. Note that there may be other sounds such as weather or humans, occasionally loud - the label does not mean that bird sound predominates.<br><br>The labels were double-annotated, by the following procedures:<br><br>* For the ff1010bird data, the presence/absence of birds was first deduced from the tags provided by the original FreeSound recordist (diverse crowdsourced tags such as "birdsong" "dawn-chorus" or "phylloscopus-collybita"), and then double-checked and refined by a manual annotator.<br><br>* For the warblrb10k data, the annotations were crowdsourced via a web service, answering the following question: "Can you hear any birds in this clip?" Annotations were then double-checked by a manual annotator to correct false-positives and -negatives.<br><br><br>To download the corresponding audio data please see http://machine-listening.eecs.qmul.ac.uk/bird-audio-detection-challenge/<br>