ArticleSensors (Basel, Switzerland)2024
Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Overcoming Data Scarcity: Few-Shot Pig Vocalization Recognition via Domain Expansion, Knowledge Transfer, and Feature Alignment.Animals : an open access journal from MDPI · 2026Article
- A Conformer-Based Time-Frequency Decoupling Network for Pig Vocalization Behavior Classification.Animals : an open access journal from MDPI · 2026Article
- Advances in Audio Classification and Artificial Intelligence for Respiratory Health and Welfare Monitoring in Swine.Biology · 2026Review
- Pig cough detection using deep features and an improved SKA-TDNN model.Tropical animal health and production · 2025Article
- APO-CViT: A Non-Destructive Estrus Detection Method for Breeding Pigs Based on Multimodal Feature Fusion.Animals : an open access journal from MDPI · 2025Article
- A review of sound-based pig monitoring for enhanced precision production.Journal of animal science and technology · 2025Review
- A Review of the Monitoring Techniques Used to Detect Oestrus in Sows.Animals : an open access journal from MDPI · 2025Review
- A novel approach to porcine abnormal sounds recognition based on improved Multi-SVDD.PloS one · 2025Article
- Underdetermined Blind Source Separation of Audio Signals for Group Reared Pigs Based on Sparse Component Analysis.Sensors (Basel, Switzerland) · 2024Article
- NmTHC: a hybrid error correction method based on a generative neural machine translation model with transfer learning.BMC genomics · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
In order to solve the problem of low recognition accuracy of traditional pig sound recognition methods, deep neural network (DNN) and Hidden Markov Model (HMM) theory were used as the basis of pig sound signal recognition in this study. In this study, the sounds made by 10 landrace pigs during eating, estrus, howling, humming and panting were collected and preprocessed by Kalman filtering and an improved endpoint detection algorithm based on empirical mode decomposition-Teiger energy operator (EMD-TEO) cepstral distance. The extracted 39-dimensional mel-frequency cepstral coefficients (MFCCs) were then used as a dataset for network learning and recognition to build a DNN- and HMM-based sound recognition model for pig states. The results show that in the pig sound dataset, the recognition accuracy of DNN-HMM reaches 83%, which is 22% and 17% higher than that of the baseline models HMM and GMM-HMM, and possesses a better recognition effect. In a sub-dataset of the publicly available dataset AudioSet, DNN-HMM achieves a recognition accuracy of 79%, which is 8% and 4% higher than the classical models SVM and ResNet18, respectively, with better robustness.
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Registered trials
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