PHmatch: A prototype-driven semi-supervised framework for insect sound recognition
Year: 2025
Type: article
Abstract: The high cost of expert annotation is a challenge in automated insect sound recognition, making semi-supervised learning (SSL) a promising approach. However, mainstream SSL frameworks are not optimized for the fine-grained nature and pulse-harmonic structures of insect spectrograms, which can lead to error propagation. To address these limitations, we propose PHmatch, an SSL framework. It integrates three components: Dynamic Prototype Regularization (DPR) to enhance feature separability, Prototype-Guided Gating (PGG) to improve pseudo-label quality, and a Pulse-Harmonic Synergistic Attention (PHSA) module to capture critical acoustic structures. Experiments on the public InsectSet66 and our InsectAcousticData24 datasets demonstrate that PHmatch achieves more discriminative feature representations and reduces error propagation for fine-grained insect sound classification.
Source: Knowledge-Based Systems
Authors: Yinzi Xu, Wei Xiao, Jie Ren, Yingxiang Peng, Yurong Sun
Institution: Central South University of Forestry and Technology
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,430
Award: G8951479130
OpenAlex ID: W4416119161