ArticleAnimals : an open access journal from MDPI2021
Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering.
Article in Animals : an open access journal from MDPI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
29 citing papers in PubMed, 87 citations in OpenAlex.
- Smart Animal Welfare: A Review of Sensing Technologies, Deployment Challenges, and AI-Driven Insights.Sensors (Basel, Switzerland) · 2026Review
- Overcoming Data Scarcity: Few-Shot Pig Vocalization Recognition via Domain Expansion, Knowledge Transfer, and Feature Alignment.Animals : an open access journal from MDPI · 2026Article
- Classification of Goat Vocalization via Lightweight Machine Learning and High-Dimensional Acoustic Features.Animals : an open access journal from MDPI · 2026Article
- Navigating AI deployment in precision livestock farming: current trends and future prospects.Animal frontiers : the review magazine of animal agriculture · 2026Article
- - Invited Review - Advancing precision livestock farming: integrating artificial intelligence and emerging technologies for sustainable livestock management.Animal bioscience · 2026Article
- A Cattle Behavior Recognition Method Based on Graph Neural Network Compression on the Edge.Animals : an open access journal from MDPI · 2026Article
- Cattle Farming Activity Monitoring Using Advanced Deep Learning Approach.Sensors (Basel, Switzerland) · 2026Article
- Advances in Audio Classification and Artificial Intelligence for Respiratory Health and Welfare Monitoring in Swine.Biology · 2026Review
- ASAS-NANP Symposium: Mathematical modeling in animal nutrition: revolutionizing animal farming with artificial intelligence: trends, challenges, and opportunities.Journal of animal science · 2026Review
- Review
- Multi-QuadEmoNet: cat and dog emotion classification model from animal vocalization using multi-stage LSTM-GRU paradigm.Frontiers in veterinary science · 2026Article
- Leveraging artificial intelligence in bioacoustics for animal health monitoring and early diagnosis in veterinary medicine.Frontiers in veterinary science · 2026Review
- Fuzzy Logic-Based Decision Support for Dairy Cattle Welfare Integrating Different Benchmarks.Animals : an open access journal from MDPI · 2025Article
- Domain general noise reduction for time series signals with Noisereduce.Scientific reports · 2025Article
- Decoding Poultry Welfare from Sound-A Machine Learning Framework for Non-Invasive Acoustic Monitoring.Sensors (Basel, Switzerland) · 2025Article
- A review of sound-based pig monitoring for enhanced precision production.Journal of animal science and technology · 2025Review
- Demographic identification of Greater Caribbean manatees via acoustic feature learning.Frontiers in artificial intelligence · 2025Article
- Voice Analysis in Dogs with Deep Learning: Development of a Fully Automatic Voice Analysis System for Bioacoustics Studies.Sensors (Basel, Switzerland) · 2024Article
- BovineTalk: machine learning for vocalization analysis of dairy cattle under the negative affective state of isolation.Frontiers in veterinary science · 2024Article
- Daylong acoustic recordings of grazing and rumination activities in dairy cows.Scientific data · 2023Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.