  This paper studies the detection of bird calls in audio segments using
stacked convolutional and recurrent neural networks. Data augmentation by
blocks mixing and domain adaptation using a novel method of test mixing are
proposed and evaluated in regard to making the method robust to unseen data.
The contributions of two kinds of acoustic features (dominant frequency and log
mel-band energy) and their combinations are studied in the context of bird
audio detection. Our best achieved AUC measure on five cross-validations of the
development data is 95.5% and 88.1% on the unseen evaluation data.
