Chirping up the Right Tree: Incorporating Biological Taxonomies into Deep Bioacoustic Classifiers

By Jason Cramer, Vincent Lostanlen, Andrew Farnsworth, Justin Salamon, Juan Pablo Bello. 2020

33 citations recorded by the source corpus.

Class imbalance in the training data hinders the generalization ability of machine listening systems. In the context of bioacoustics, this issue may be circumvented by aggregating species labels into super-groups of higher taxonomic rank: genus, family, order, and so forth. However, different applications of machine listening to wildlife monitoring may require different levels of granularity. This paper introduces TaxoNet, a deep neural network for structured classification of signals from living organisms. TaxoNet is trained as a multitask and multilabel model, following a new architectural principle in end-to-end learning named "hierarchical composition": shallow layers extract a shared representation to predict a root taxon, while deeper layers specialize recursively to lower-rank taxa. In this way, TaxoNet is capable of handling taxonomic uncertainty, out-of-vocabulary labels, and open-set deployment settings. An experimental benchmark on two new bioacoustic datasets (ANAFCC and BirdVox-14SD) leads to state-of-the-art results in bird species classification. Furthermore, on a task of coarse-grained classification, TaxoNet also outperforms a flat single-task model trained on aggregate labels.

Subfields: Developmental Biology, Ecology, Genetics

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Dan Stowell

Citations in the corpus

FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, methodology): “…approaches using audio and text information (e.g., using the provided Freesound tags, title, and textual description for the clips) [91, 92]; evaluation of hierarchical classiﬁcation via ontology-aware learning frameworks [5, 93, 94]; or approaches speciﬁcally combining strong and weak labels [95].”
Computational bioacoustics with deep learning: a review and roadmap (2021, methodology): “multi-task learning—another form of transfer learning, this involves training on multiple tasks simultaneously (Morfi & Stowell, 2018; Zeghidour et al., 2021; Cramer et al., 2020);”
BirdNET: A deep learning solution for avian diversity monitoring (2021, background): “5 score of 0.500 across six owl species, (LeBien et al., 2020) reported a mAP of 0.893 across 24 species of birds and frogs, and (Cramer et al., 2020) could detect flight calls with a micro-average accuracy of 0.663 across 14 species.”