On December 5th, 2019, BirdVox researcher Vincent Lostanlen gave a 20-minute talk in San Diego, CA, USA, by invitation of the 178th meeting of the Acoustical Society of America (ASA). The talk was entitled “BirdVox: From flight call classification to full-season migration monitoring”, and was featured in the special session on machine learning for animal bioacoustics.
We reproduce the abstract below. This abstract appeared on the latest issue of the Journal of the Acoustical Society of America (JASA), volume 146, number 4, page 2984.
The BirdVox project aims at inventing new machine listening methods for the bioacoustic analysis of avian migration at the continental scale. It relies on an acoustic sensor network of low-cost, autonomous recording units to detect nocturnal flight calls and classify them in terms of family, genus, and species. As a result, each sensor produces a daily checklist of the species currently aloft, next to their respective individual counts. In this talk, I describe the research methods of BirdVox and their implications for advancing the understanding of animal behavior and conservation biology. The commonality of these methods is that they tightly integrate data-driven components alongside the induction of domain-specific knowledge. Furthermore, the resort to machine learning is not restricted to supervised acoustic event classification tasks, but also encompasses audio representation learning, few-shot active learning for efficient annotation, and Bayesian inference for adapting to multiple acoustic environments. I conclude with an overview of some open-source software tools for large-scale bioacoustics: librosa (spectrogram analysis), pysox (audio transformations), JAMS (rich annotation of audio events), muda (data augmentation), scaper (soundscape synthesis), pescador (stochastic sampling), and mir_eval (evaluation).
https://asa.scitation.org/doi/abs/10.1121/1.5137328
