  Analyses for biodiversity monitoring based on passive acoustic monitoring
(PAM) recordings is time-consuming and challenged by the presence of background
noise in recordings. Existing models for sound event detection (SED) worked
only on certain avian species and the development of further models required
labeled data. The developed framework automatically extracted labeled data from
available platforms for selected avian species. The labeled data were embedded
into recordings, including environmental sounds and noise, and were used to
train convolutional recurrent neural network (CRNN) models. The models were
evaluated on unprocessed real world data recorded in urban KwaZulu-Natal
habitats. The Adapted SED-CRNN model reached a F1 score of 0.73, demonstrating
its efficiency under noisy, real-world conditions. The proposed approach to
automatically extract labeled data for chosen avian species enables an easy
adaption of PAM to other species and habitats for future conservation projects.
