  This paper proposes to perform unsupervised detection of bioacoustic events
by pooling the magnitudes of spectrogram frames after per-channel energy
normalization (PCEN). Although PCEN was originally developed for speech
recognition, it also has beneficial effects in enhancing animal vocalizations,
despite the presence of atmospheric absorption and intermittent noise. We prove
that PCEN generalizes logarithm-based spectral flux, yet with a tunable time
scale for background noise estimation. In comparison with pointwise logarithm,
PCEN reduces false alarm rate by 50x in the near field and 5x in the far field,
both on avian and marine bioacoustic datasets. Such improvements come at
moderate computational cost and require no human intervention, thus heralding a
promising future for PCEN in bioacoustics.
