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A Lightweight Birdsong Recognition Approach Based on FCNM-NET

Jian Zheng 1, Yang Wang 2,* and Hui Zeng 2
1 Yichun Lithium New Energy Industry Research Institute, Jiangxi University of Science and Technology, Yichun, China 2 Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control, School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China * Correspondence: Yang Wang, Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control, School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China

Vol. 28 (2026): 2026 2nd International Conference on Agricultural Sciences, Economics, Biomedical and Environmental Sciences (SEMBE 2026)

Received: 2026-07-18

Accepted: 2026-07-18

Published: 2026-07-18

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Downloads: 292

Abstract

As an essential technique in ecological monitoring and wildlife protection, birdsong recognition contributes significantly to safeguarding bird species diversity. However, in practical applications, birdsong recognition often encounters two major challenges: limited device resources and complex environmental noise. In response to the challenge of achieving a balance between model compactness and recognition accuracy, this paper presents FCNM-NET, a lightweight neural network for birdsong recognition based on MobileNetV3. First, a multi-scale feature fusion module is introduced, processing the input spectrogram through three parallel branches and merging their outputs to enhance the representation of diverse acoustic patterns. Furthermore, to optimize parameter efficiency and promote more compact feature learning, this paper replaces the original 5×5 convolution in MobileNetV3 with an improved, structurally optimized Nonlinear Activation Free (NAF) module, referred to as CANAF. This module combines lightweight attention and residual connections to balance computational cost and feature expressiveness. Finally, a 1×1 convolution is employed to replace the traditional fully connected layer in the classifier, aiming to minimize parameter overhead, and by introducing a Coordinate Attention (CA) module after global pooling to enhance channel dependency modeling and maintain recognition precision. Experimental results demonstrate that the proposed method achieves recognition accuracies of 96.24% on the self-constructed GZDataset and 94.38% on the publicly available UrbanSound8K dataset. These results demonstrate that FCNM-NET effectively addresses the lightweight model challenge in birdsong recognition while maintaining high recognition efficiency and accuracy, thereby providing strong technical support for ecological conservation efforts.

Keywords

attention mechanism birdsong recognition mobilenet feature fusion neural networks

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Copyright and License

Published in2026-07-18 17:50:55

DOI https://doi.org/10.70088/hamtgv24

Creative Commons
Copyright: © 2026 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/license s/by/4.0/).

Copyright
Copyright © The Author(s), 2026. Published by SEMBE 2026

Journal Information

  • Vol. 28 (2026): 2026 2nd International Conference on Agricultural Sciences, Economics, Biomedical and Environmental Sciences (SEMBE 2026)
  • 2026-07-18
  • ISSN: (Print) 3078-770X/ (Online) 3078-7718
  • Journal Homepage

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