"The task is to design a system that, given a short audio recording, returns a binary decision for the presence/absence of bird sound (bird sound of any kind). The output can be just "0" or "1", but we encourage weighted/probability outputs in the continuous range [0,1] for the purposes of evaluation. For the main assessment we will use the well-known "Area Under the ROC Curve" (AUC) measure of classification performance.
An important goal of this task is generalisation to new conditions. To explore this we provide 3 separate development datasets, and 3 evaluation datasets, each recorded under differing conditions. The datasets will have different balances of positive/negative cases, different bird species, different background sounds, different recording equipment. To solve this task well, you will need an approach which either inherently generalises across conditions (including conditions not seen in the training data), or which can self-adapt to new datasets ("domain adaptation").
Note that for every audio clip, you will be told which dataset it belongs to. This means that adapting to the overall characteristics of each dataset separately is possible. The evaluation will also consider each dataset separately and combine the outcomes, rather than treating them as a single pooled dataset."
Challenge link: http://dcase.community/challenge2018/task-bird-audio-detection