On May 5th, 2020, PhD candidate Jason Cramer presented a new paper by the BirdVox team to the attendees of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).

We reproduce the abstract of the paper below.

 

Chirping up the right tree: Incorporating biological taxonomies in deep bioacoustic classifiers

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

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.

 

We have uploaded the video of Jason’s presentation to YouTube:

 

The preprint of the ICASSP paper can be found at: http://www.justinsalamon.com/uploads/4/3/9/4/4394963/cramer_taxonet_icassp_2020.pdf

 

The BirdVox-ANAFCC dataset can be downloaded on Zenodo:
https://zenodo.org/record/3666782#.XxG5LJNzS1s

 

The source code for the TaxoNet deep learning system can be cloned from GitHub:
https://github.com/BirdVox/birdvoxclassify