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Pan W, Li H, Zhou X, Jiao J, Zhu C, Zhang Q. Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models. SENSORS (BASEL, SWITZERLAND) 2024; 24:1269. [PMID: 38400427 PMCID: PMC10891870 DOI: 10.3390/s24041269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 02/04/2024] [Accepted: 02/15/2024] [Indexed: 02/25/2024]
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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Affiliation(s)
- Weihao Pan
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
| | - Hualong Li
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China
| | - Xiaobo Zhou
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
| | - Jun Jiao
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
| | - Cheng Zhu
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
| | - Qiang Zhang
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
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Hou Y, Li Q, Wang Z, Liu T, He Y, Li H, Ren Z, Guo X, Yang G, Liu Y, Yu L. Study on a Pig Vocalization Classification Method Based on Multi-Feature Fusion. SENSORS (BASEL, SWITZERLAND) 2024; 24:313. [PMID: 38257406 PMCID: PMC10819726 DOI: 10.3390/s24020313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/28/2023] [Accepted: 01/03/2024] [Indexed: 01/24/2024]
Abstract
To improve the classification of pig vocalization using vocal signals and improve recognition accuracy, a pig vocalization classification method based on multi-feature fusion is proposed in this study. With the typical vocalization of pigs in large-scale breeding houses as the research object, short-time energy, frequency centroid, formant frequency and first-order difference, and Mel frequency cepstral coefficient and first-order difference were extracted as the fusion features. These fusion features were improved using principal component analysis. A pig vocalization classification model with a BP neural network optimized based on the genetic algorithm was constructed. The results showed that using the improved features to recognize pig grunting, squealing, and coughing, the average recognition accuracy was 93.2%; the recognition precisions were 87.9%, 98.1%, and 92.7%, respectively, with an average of 92.9%; and the recognition recalls were 92.0%, 99.1%, and 87.4%, respectively, with an average of 92.8%, which indicated that the proposed pig vocalization classification method had good recognition precision and recall, and could provide a reference for pig vocalization information feedback and automatic recognition.
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Affiliation(s)
- Yuting Hou
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; (Y.H.); (Q.L.); (H.L.); (Z.R.); (X.G.); (G.Y.)
- School of Science, China University of Geosciences (Beijing), Beijing 100083, China;
| | - Qifeng Li
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; (Y.H.); (Q.L.); (H.L.); (Z.R.); (X.G.); (G.Y.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Zuchao Wang
- School of Science, China University of Geosciences (Beijing), Beijing 100083, China;
| | - Tonghai Liu
- College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300384, China; (T.L.); (Y.H.)
| | - Yuxiang He
- College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300384, China; (T.L.); (Y.H.)
| | - Haiyan Li
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; (Y.H.); (Q.L.); (H.L.); (Z.R.); (X.G.); (G.Y.)
| | - Zhiyu Ren
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; (Y.H.); (Q.L.); (H.L.); (Z.R.); (X.G.); (G.Y.)
| | - Xiaoli Guo
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; (Y.H.); (Q.L.); (H.L.); (Z.R.); (X.G.); (G.Y.)
| | - Gan Yang
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; (Y.H.); (Q.L.); (H.L.); (Z.R.); (X.G.); (G.Y.)
| | - Yu Liu
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; (Y.H.); (Q.L.); (H.L.); (Z.R.); (X.G.); (G.Y.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Ligen Yu
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; (Y.H.); (Q.L.); (H.L.); (Z.R.); (X.G.); (G.Y.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
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Netzley AH, Pelled G. The Pig as a Translational Animal Model for Biobehavioral and Neurotrauma Research. Biomedicines 2023; 11:2165. [PMID: 37626662 PMCID: PMC10452425 DOI: 10.3390/biomedicines11082165] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023] Open
Abstract
In recent decades, the pig has attracted considerable attention as an important intermediary model animal in translational biobehavioral research due to major similarities between pig and human neuroanatomy, physiology, and behavior. As a result, there is growing interest in using pigs to model many human neurological conditions and injuries. Pigs are highly intelligent and are capable of performing a wide range of behaviors, which can provide valuable insight into the effects of various neurological disease states. One area in which the pig has emerged as a particularly relevant model species is in the realm of neurotrauma research. Indeed, the number of investigators developing injury models and assessing treatment options in pigs is ever-expanding. In this review, we examine the use of pigs for cognitive and behavioral research as well as some commonly used physiological assessment methods. We also discuss the current usage of pigs as a model for the study of traumatic brain injury. We conclude that the pig is a valuable animal species for studying cognition and the physiological effect of disease, and it has the potential to contribute to the development of new treatments and therapies for human neurological and psychiatric disorders.
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Affiliation(s)
- Alesa H. Netzley
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI 48824, USA;
| | - Galit Pelled
- Neuroscience Program, Michigan State University, East Lansing, MI 48824, USA
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48824, USA
- Department of Radiology, Michigan State University, East Lansing, MI 48824, USA
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Lagua EB, Mun HS, Ampode KMB, Chem V, Kim YH, Yang CJ. Artificial Intelligence for Automatic Monitoring of Respiratory Health Conditions in Smart Swine Farming. Animals (Basel) 2023; 13:1860. [PMID: 37889795 PMCID: PMC10251864 DOI: 10.3390/ani13111860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/31/2023] [Accepted: 05/31/2023] [Indexed: 10/29/2023] Open
Abstract
Porcine respiratory disease complex is an economically important disease in the swine industry. Early detection of the disease is crucial for immediate response to the disease at the farm level to prevent and minimize the potential damage that it may cause. In this paper, recent studies on the application of artificial intelligence (AI) in the early detection and monitoring of respiratory disease in swine have been reviewed. Most of the studies used coughing sounds as a feature of respiratory disease. The performance of different models and the methodologies used for cough recognition using AI were reviewed and compared. An AI technology available in the market was also reviewed. The device uses audio technology that can monitor and evaluate the herd's respiratory health status through cough-sound recognition and quantification. The device also has temperature and humidity sensors to monitor environmental conditions. It has an alarm system based on variations in coughing patterns and abrupt temperature changes. However, some limitations of the existing technology were identified. Substantial effort must be exerted to surmount the limitations to have a smarter AI technology for monitoring respiratory health status in swine.
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Affiliation(s)
- Eddiemar B. Lagua
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (E.B.L.); (H.-S.M.); (K.M.B.A.); (V.C.)
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, 255 Jungangno, Suncheon 57922, Republic of Korea
| | - Hong-Seok Mun
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (E.B.L.); (H.-S.M.); (K.M.B.A.); (V.C.)
- Department of Multimedia Engineering, Sunchon National University, Suncheon 57922, Republic of Korea
| | - Keiven Mark B. Ampode
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (E.B.L.); (H.-S.M.); (K.M.B.A.); (V.C.)
- Department of Animal Science, College of Agriculture, Sultan Kudarat State University, Tacurong City 9800, Philippines
| | - Veasna Chem
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (E.B.L.); (H.-S.M.); (K.M.B.A.); (V.C.)
| | - Young-Hwa Kim
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Chonnam National University, Gwangju 61186, Republic of Korea;
| | - Chul-Ju Yang
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (E.B.L.); (H.-S.M.); (K.M.B.A.); (V.C.)
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, 255 Jungangno, Suncheon 57922, Republic of Korea
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