  Detecting bird sounds in audio recordings automatically, if accurate enough,
is expected to be of great help to the research community working in bio- and
ecoacoustics, interested in monitoring biodiversity based on audio field
recordings. To estimate how accurate the state-of-the-art machine learning
approaches are, the Bird Audio Detection challenge involving large audio
datasets was recently organized. In this paper, experiments using several types
of convolutional neural networks (i.e. standard CNNs, residual nets and densely
connected nets) are reported in the framework of this challenge. DenseNets were
the preferred solution since they were the best performing and most compact
models, leading to a 88.22% area under the receiver operator curve score on the
test set of the challenge. Performance gains were obtained thank to data
augmentation through time and frequency shifting, model parameter averaging
during training and ensemble methods using the geometric mean. On the contrary,
the attempts to enlarge the training dataset with samples of the test set with
automatic predictions used as pseudo-groundtruth labels consistently degraded
performance.
