Audiovisual media are increasingly used to study the communication and behavior of animal groups, e.g. by placing microphones in the animals habitat resulting in huge datasets with only a small amount of animal interactions. The Orcalab has recorded orca whales since 1973 using stationary underwater hydrophones and made it publicly available on the Orchive. There exist over 15 000 manually extracted orca/noise annotations and about 20 000 h unseen audio data. To analyze the behavior and communication of killer whales we need to interpret the different call types. In this work, we present a two-stage classification approach using the labeled call/noise files and a few labeled call-type files. Results indicate a reliable accuracy of 95.0 % for call segmentation and 87 % for classification of 12 call classes. We further visualize the learned orca call representations in the convolutional neural network (CNN) activations to explain the potential of CNN based recognition for bioacoustic signals.