  In the field of wildlife observation and conservation, approaches involving
machine learning on audio recordings are becoming increasingly popular.
Unfortunately, available datasets from this field of research are often not
optimal learning material; Samples can be weakly labeled, of different lengths
or come with a poor signal-to-noise ratio. In this work, we introduce a
generalized approach that first relabels subsegments of MEL spectrogram
representations, to achieve higher performances on the actual multi-class
classification tasks. For both the binary pre-sorting and the classification,
we make use of convolutional neural networks (CNN) and various
data-augmentation techniques. We showcase the results of this approach on the
challenging \textit{ComparE 2021} dataset, with the task of classifying between
different primate species sounds, and report significantly higher Accuracy and
UAR scores in contrast to comparatively equipped model baselines.
