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. More info: http://machine-listening.eecs.qmul.ac.uk/bird-audio-detection-challenge/ =============== REUSE: 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: Dan Stowell, Mike Wood, Yannis Stylianou, Hervé Glotin. Bird detection in audio: a survey and a challenge, in Proceedings of MLSP 2016, arXiv:1608.03417 [cs.SD], 2016. 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 =============== How the annotations were collected: 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. The labels were double-annotated, by the following procedures: * 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. * 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. To download the corresponding audio data please see http://machine-listening.eecs.qmul.ac.uk/bird-audio-detection-challenge/