  Many approaches have been used in bird species classification from their
sound in order to provide labels for the whole of a recording. However, a more
precise classification of each bird vocalization would be of great importance
to the use and management of sound archives and bird monitoring. In this work,
we introduce a technique that using a two step process can first automatically
detect all bird vocalizations and then, with the use of 'weakly' labelled
recordings, classify them. Evaluations of our proposed method show that it
achieves a correct classification of 61% when used in a synthetic dataset, and
up to 89% when the synthetic dataset only consists of vocalizations larger than
1000 pixels.
