Voiceprint Recognition Based on Big Data and Gaussian Mixture Model

Gu, Yueze; Shi, Aining; Ma, Ruichen

doi:10.1109/icsgea53208.2021.00065

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Voiceprint recognition is a technology that identifies individuals by analyzing the characteristics of their voice, which has been extensively employed in human speech recognition [5,6] and identity verification [7,8]. Research in voiceprint recognition commenced in the 1950s, yet in the realm of animal production [9], notably pig farming, the research and application of voiceprint technology has been significantly delayed and remains in its nascent stage.…”

Section: Introductionmentioning

confidence: 99%

Animal Vocal Recognition-Based Breeding Tracking and Disease Warning

Yinggang Xie,

Yangpeng Xiao

et al. 2023

Journal of Computers

View full text Add to dashboard Cite

<p>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.</p> <p> </p>

show abstract