  The Orchive is a large collection of over 20,000 hours of audio recordings
from the OrcaLab research facility located off the northern tip of Vancouver
Island. It contains recorded orca vocalizations from the 1980 to the present
time and is one of the largest resources of bioacoustic data in the world. We
have developed a web-based interface that allows researchers to listen to these
recordings, view waveform and spectral representations of the audio, label
clips with annotations, and view the results of machine learning classifiers
based on automatic audio features extraction. In this paper we describe such
classifiers that discriminate between background noise, orca calls, and the
voice notes that are present in most of the tapes. Furthermore we show
classification results for individual calls based on a previously existing orca
call catalog. We have also experimentally investigated the scalability of
classifiers over the entire Orchive.
