  Open audio databases such as Xeno-Canto are widely used to build datasets to
explore bird song repertoire or to train models for automatic bird sound
classification by deep learning algorithms. However, such databases suffer from
the fact that bird sounds are weakly labelled: a species name is attributed to
each audio recording without timestamps that provide the temporal localization
of the bird song of interest. Manual annotations can solve this issue, but they
are time consuming, expert-dependent, and cannot run on large datasets. Another
solution consists in using a labelling function that automatically segments
audio recordings before assigning a label to each segmented audio sample.
Although labelling functions were introduced to expedite strong label
assignment, their classification performance remains mostly unknown. To address
this issue and reduce label noise (wrong label assignment) in large bird song
datasets, we introduce a data-centric novel labelling function composed of
three successive steps: 1) time-frequency sound unit segmentation, 2) feature
computation for each sound unit, and 3) classification of each sound unit as
bird song or noise with either an unsupervised DBSCAN algorithm or the
supervised BirdNET neural network. The labelling function was optimized,
validated, and tested on the songs of 44 West-Palearctic common bird species.
We first showed that the segmentation of bird songs alone aggregated from 10%
to 83% of label noise depending on the species. We also demonstrated that our
labelling function was able to significantly reduce the initial label noise
present in the dataset by up to a factor of three. Finally, we discuss
different opportunities to design suitable labelling functions to build
high-quality animal vocalizations with minimum expert annotation effort.
