  It is easier to hear birds than see them, however, they still play an
essential role in nature and they are excellent indicators of deteriorating
environmental quality and pollution. Recent advances in Machine Learning and
Convolutional Neural Networks allow us to detect and classify bird sounds, by
doing this, we can assist researchers in monitoring the status and trends of
bird populations and biodiversity in ecosystems. We propose a sound detection
and classification pipeline for analyzing complex soundscape recordings and
identify birdcalls in the background. Our pipeline learns from weak labels,
classifies fine-grained bird vocalizations in the wild, and is robust against
background sounds (e.g., airplanes, rain, etc). Our solution achieved 10th
place of 816 teams at the BirdCLEF 2021 Challenge hosted on Kaggle.
