无线电通信技术2024，Vol.50Issue(4)：789-798,10.DOI:10.3969/j.issn.1003-3114.2024.04.022
基于ERes-ECAM的动物声纹识别
Animal Voiceprint Recognition Based on ERes-ECAM
摘要
Abstract
Voiceprint recognition technology is not only widely used in the field of human identity verification,but also has made some progress in animal species recognition.Existing models suffer from insufficient feature expression ability,while the time complexi-ty and inference speed of the models need to be optimized under the premise of guaranteeing performance.In this paper,we proposed a novel architecture of Enhanced Res2block connected Enhanced Context Aware Masking(ERes-ECAM)for vocal animal embedding learning,which adopts Densely-connected Time Delay Neural Network(D-TDNN)as the backbone,and in order to solve the problem of fuzzy irrelevant noise while being able to extract more effective key information,an Enhanced Context Aware Masking(ECAM)mod-ule with a multi-granularity pooling method is used in the D-TDNN layer,and the front-end is connected to a residual module,and the features extracted within the residual block are fused to extract local information by means of Local Feature Fusion(LFF),which im-proves the accuracy and robustness of the voiceprint verification system.As described in this paper,experiments were conducted in two test sets,Anim-Celeb and Pig-Celeb,and experimental results showed that the Equal Error Rate(EER)of the proposed architecture reached 6.88％and 7.24％,respectively,and at the same time,the accuracies of recognizing the animal species and the pig species reached 93.12％and 92.76％.关键词
深度学习/声纹识别/上下文感知掩码/局部特征融合/动物种类识别Key words
deep learning/voiceprint recognition/context aware masking/LFF/animal species recognition分类
信息技术与安全科学引用本文复制引用
侯卫民,孙艺菲,刘峻滔..基于ERes-ECAM的动物声纹识别[J].无线电通信技术,2024,50(4):789-798,10.基金项目
河北省省级科技计划项目(20355901D,21355901D)Hebei Provincial Science and Technology Program(20355901D,21355901D) （20355901D,21355901D）
