Long-distance Detection of Bioacoustic Events with Per-channel Energy Normalization
This addresses the problem of detecting animal vocalizations in noisy, long-distance environments for bioacoustics researchers, offering a moderate incremental improvement over existing methods.
The paper tackles unsupervised detection of bioacoustic events by applying per-channel energy normalization (PCEN) to spectrograms, reducing false alarm rates by 50x in near field and 5x in far field on avian and marine datasets.
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.