  Animal vocalisations and natural soundscapes are fascinating objects of
study, and contain valuable evidence about animal behaviours, populations and
ecosystems. They are studied in bioacoustics and ecoacoustics, with signal
processing and analysis an important component. Computational bioacoustics has
accelerated in recent decades due to the growth of affordable digital sound
recording devices, and to huge progress in informatics such as big data, signal
processing and machine learning. Methods are inherited from the wider field of
deep learning, including speech and image processing. However, the tasks,
demands and data characteristics are often different from those addressed in
speech or music analysis. There remain unsolved problems, and tasks for which
evidence is surely present in many acoustic signals, but not yet realised. In
this paper I perform a review of the state of the art in deep learning for
computational bioacoustics, aiming to clarify key concepts and identify and
analyse knowledge gaps. Based on this, I offer a subjective but principled
roadmap for computational bioacoustics with deep learning: topics that the
community should aim to address, in order to make the most of future
developments in AI and informatics, and to use audio data in answering
zoological and ecological questions.
