  We present a multi-modal Deep Neural Network (DNN) approach for bird song
identification. The presented approach takes both audio samples and metadata as
input. The audio is fed into a Convolutional Neural Network (CNN) using four
convolutional layers. The additionally provided metadata is processed using
fully connected layers. The flattened convolutional layers and the fully
connected layer of the metadata are joined and fed into a fully connected
layer. The resulting architecture achieved 2., 3. and 4. rank in the
BirdCLEF2017 task in various training configurations.
