  Quantitative and qualitative analysis of acoustic backscattered signals from
the seabed bottom to the sea surface is used worldwide for fish stocks
assessment and marine ecosystem monitoring. Huge amounts of raw data are
collected yet require tedious expert labeling. This paper focuses on a case
study where the ground truth labels are non-obvious: echograms labeling, which
is time-consuming and critical for the quality of fisheries and ecological
analysis. We investigate how these tasks can benefit from supervised learning
algorithms and demonstrate that convolutional neural networks trained with
non-stationary datasets can be used to stress parts of a new dataset needing
human expert correction. Further development of this approach paves the way
toward a standardization of the labeling process in fisheries acoustics and is
a good case study for non-obvious data labeling processes.
