In:
The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 146, No. 4_Supplement ( 2019-10-01), p. 2984-2984
Abstract:
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 mireval (evaluation).
Type of Medium:
Online Resource
ISSN:
0001-4966
,
1520-8524
Language:
English
Publisher:
Acoustical Society of America (ASA)
Publication Date:
2019
detail.hit.zdb_id:
1461063-2
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