Beyond amplitude:Phase integration in bird vocalization recognition with MHAResNet  

作　　者：Jiangjian Xie Zhulin Hao Chunhe Hu Changchun Zhang Junguo Zhang 

机构地区：[1]School of Technology,Beijing Forestry University,Beijing,100083,China [2]State Key Laboratory of Efficient Production of Forest Resources,Beijing Forestry University,Beijing,100083,China [3]Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation,Beijing,100083,China [4]Research Center for Biodiversity Intelligent Monitoring,Beijing Forestry University,Beijing,100083,China

出　　处：《Avian Research》2025年第1期119-128,共10页鸟类学研究(英文版)

基　　金：supported by the Beijing Natural Science Foundation (5252014);the National Natural Science Foundation of China (62303063)。

摘　　要：Bird vocalizations are pivotal for ecological monitoring,providing insights into biodiversity and ecosystem health.Traditional recognition methods often neglect phase information,resulting in incomplete feature representation.In this paper,we introduce a novel approach to bird vocalization recognition(BVR)that integrates both amplitude and phase information,leading to enhanced species identification.We propose MHARes Net,a deep learning(DL)model that employs residual blocks and a multi-head attention mechanism to capture salient features from logarithmic power(POW),Instantaneous Frequency(IF),and Group Delay(GD)extracted from bird vocalizations.Experiments on three bird vocalization datasets demonstrate our method's superior performance,achieving accuracy rates of 94%,98.9%,and 87.1%respectively.These results indicate that our approach provides a more effective representation of bird vocalizations,outperforming existing methods.This integration of phase information in BVR is innovative and significantly advances the field of automatic bird monitoring technology,offering valuable tools for ecological research and conservation efforts.

关 键 词：Bird vocalization recognition Feature fusion Phase information Residual network 

分 类 号：Q62[生物学—生物物理学]