  Autonomous recording units and passive acoustic monitoring present minimally
intrusive methods of collecting bioacoustics data. Combining this data with
species agnostic bird activity detection systems enables the monitoring of
activity levels of bird populations. Unfortunately, variability in ambient
noise levels and subject distance contribute to difficulties in accurately
detecting bird activity in recordings. The choice of acoustic frontend directly
affects the impact these issues have on system performance. In this paper, we
benchmark traditional fixed-parameter acoustic frontends against the new
generation of learnable frontends on a wide-ranging bird audio detection task
using data from the DCASE2018 BAD Challenge. We observe that Per-Channel Energy
Normalization is the best overall performer, achieving an accuracy of 89.9%,
and that in general learnable frontends significantly outperform traditional
methods. We also identify challenges in learning filterbanks for bird audio.
