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篇名
Animal Vocal Recognition-Based Breeding Tracking and Disease Warning
並列篇名
Animal Vocal Recognition-Based Breeding Tracking and Disease Warning
作者 Yinggang Xie (Yinggang Xie)Yangpeng Xiao (Yangpeng Xiao)Xuewei Peng (Xuewei Peng)Qijia Liu (Qijia Liu)
英文摘要

In this study, we investigate the application of a deep learning framework for the recognition of pig vocalizations. This innovative approach aims to actively monitor and evaluate the diverse states of pigs, with an overarching objective to improve the efficiency of pig farming through prompt identification and resolution of issues. In our comprehensive data collection effort, we carefully gathered a vast assortment of vocal samples from 50 pigs, representative of four distinct states: normal, frightened, coughing, and sneezing. We then meticulously analyzed this vocal data using Mel Frequency Cepstral Coefficients (MFCC). For accurate recognition of pig vocalizations, we devised a fusion model that combines the strengths of Residual Networks (ResNet) and Long Short-Term Memory Networks (LSTM). This model was subsequently tailored, trained, and optimized to meet our specific requirements. Upon rigorous evaluation, we found our model to exhibit exceptional performance in pig vocal recognition tasks, thereby reinforcing the potential of deep learning methodologies in revolutionizing the livestock industry. This research notably underscores the potential of deploying efficient real-time health monitoring systems, offering a promising avenue towards modernizing livestock management practices.

 

起訖頁 127-143
關鍵詞 deep learningpig vocal recognitionMFCCResNetreal-time health monitoring
刊名 電腦學刊  
期數 202308 (34:4期)
該期刊-上一篇 A Method for Assembly Accuracy Detection and Intelligent Error Estimation Based on Computer Vision
該期刊-下一篇 Computer Vision Aided Pantograph Fault Identification Method for Multiple Units
 

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