华南农业大学学报2024，Vol.45Issue(5)：730-742,13.DOI:10.7671/j.issn.1001-411X.202312011
基于欠定盲源分离和深度学习的生猪状态音频识别
Pig state audio recognition based on underdetermined blind source separation and deep learning
摘要
Abstract
[Objective]In order to solve the problem of difficult separation and recognition of pig audio under group rearing environment,we propose a method of pig state audio recognition based on underdetermined blind source separation and ECA-EfficientNetV2.[Method]Four types of pig audio signals were simulated as observation signals in group rearing environment.After the signals were sparsely represented,the signal mixing matrix was estimated by hierarchical clustering,and the lp-paradigm reconstruction algorithm was used to solve for the minimum of lp-paradigm to complete the reconstruction of pig audio signals.The reconstructed signals were transformed into acoustic spectrograms,which were divided into four categories,namely,eating sound,roar sound,hum sound and estrous sound.The audio was recognized using the ECA-EfficientNetV2 network model to obtain the state of the pigs.[Result]The normalized mean square error of the hybrid matrix estimation was as low as 3.266×10-4,and the signal-to-noise ratios of the separated reconstructed audio ranged from 3.254 to 4.267 dB.The acoustic spectrogram was recognized and detected by ECA-EfficientNetV2 with an accuracy of up to 98.35%,and the accuracy improved by 2.88 and 1.81 percentage points compared with the classical convolutional neural networks ResNet50 and VGG16,respectively.Compared with the original EfficientNetV2,the accuracy decreased by 0.52 percentage points,but the amount of the model parameters reduced by 33.56%,the floating-point operations(FLOPs)reduced by 1.86 G,and inference time reduced by 9.40 ms.[Conclusion]The method based on blind source separation and improvement of EfficientNetV2 lightly and efficiently realizes separating and recognizing audio signals of group-raised pigs.关键词
猪/盲源分离/声谱图/音频识别/稀疏重构/卷积神经网络Key words
Pig/Blind source separation/Spectrogram/Audio recognition/Sparse reconstruction/Convolutional neural network分类
信息技术与安全科学引用本文复制引用
潘伟豪,盛卉子,王春宇,闫顺丕,周小波,辜丽川,焦俊..基于欠定盲源分离和深度学习的生猪状态音频识别[J].华南农业大学学报,2024,45(5):730-742,13.基金项目
安徽省重点研究与开发计划(2023n06020051,202103B06020013) （2023n06020051,202103B06020013）
安徽省研究生质量工程项目(2022lhpysfjd023,2022cxcyjs010) （2022lhpysfjd023,2022cxcyjs010）
