Decoding insect song: a multitask semisupervised Orthoptera bioacoustic classifier

Conference item

Passive acoustic monitoring holds great promise for ecological inference, yet existing automated tools are typically narrowly trained and nontransferable. We address these limitations with PULSE, a semi-supervised, multi-task framework for Orthoptera bioacoustics, combining weaklysupervised species classification, self-supervised learning on unlabelled field audio, and knowledge distillation from a general-purpose bioacoustic model. Our domain-adapted specialist model outperforms a state-of-the-art general model across all metrics (macro F1: 0.21 vs. 0.07; AUC: 0.74 vs. 0.45; AP: 0.32 vs. 0.19), with active learning further raising F1 to 0.34 and AUC to 0.84. Beyond classification, the learned embeddings encode ecologically meaningful structure, exposed through an interactive visualisation tool for ecological discovery.

Authors: Isupova, OI; Kuzin, D; Browning, E; Mills, T; Reece, S

• Publication status: Accepted
• Peer review status: Peer reviewed
• Acceptance date: 2026-06-01
• Event title: Machine Learning for Audio workshop at International Conference on Machine Learning (ICML) 2026
• Event location: Seoul, South Korea
• Event website: https://icml.cc/Conferences/2026
• Event start date: 2026-07-10
• Event end date: 2026-07-10
• Language: English