  Bird sounds possess distinctive spectral structure which may exhibit small
shifts in spectrum depending on the bird species and environmental conditions.
In this paper, we propose using convolutional recurrent neural networks on the
task of automated bird audio detection in real-life environments. In the
proposed method, convolutional layers extract high dimensional, local frequency
shift invariant features, while recurrent layers capture longer term
dependencies between the features extracted from short time frames. This method
achieves 88.5% Area Under ROC Curve (AUC) score on the unseen evaluation data
and obtains the second place in the Bird Audio Detection challenge.
