Pig Sound Analysis: A Measure of Welfare

doi:10.12133/j.smartag.SA202204004

Abstract

Abstract:

Pig welfare is closely related to the economical production of pig farms. With regard to pig welfare assessment, pig sounds are significant indicators, which can reflect the quality of the barn environment, the physical condition and the health of pigs. Therefore, pig sound analysis is of high priority and necessary. In this review, the relationship between pig sound and welfare was analyzed. Three kinds of pig sounds are closely related to pig welfare, including coughs, screams, and grunts. Subsequently, both wearable and non-contact sensors were briefly described in two aspects of advantages and disadvantages. Based on the advantages and feasibility of microphone sensors in contactless way, the existing techniques for processing pig sounds were elaborated and evaluated for further in-depth research from three aspects: sound recording and labeling, feature extraction, and sound classification. Finally, the challenges and opportunities of pig sound research were discussed for the ultimate purpose of precision livestock farming (PLF) in four ways: concerning sound monitoring technologies, individual pig welfare monitoring, commercial applications and pig farmers. In summary, it was found that most of the current researches on pig sound recognition tasks focused on the selection of classifiers and algorithm improvement, while fewer research was conducted on sound labeling and feature extraction. Meanwhile, pig sound recognition faces some challenging problems, involving the difficulty in obtaining the audio data from different pig growth stages and verifying the developed algorithms in a variety of pig farms. Overall, it is suggested that technologies involved in the automatic identification process should be explored in depth. In the future, strengthen cooperation among cross-disciplinary experts to promote the development and application of PLF is also nessary.

Key words: pig sound classification, animal welfare, sound analysis, feature extraction, precision livestock farming, sound monitering

CLC Number:

JI Nan, YIN Yanling, SHEN Weizheng, KOU Shengli, DAI Baisheng, WANG Guowei. Pig Sound Analysis: A Measure of Welfare[J]. Smart Agriculture, 2022, 4(2): 19-35.

Figures/Tables 3

Table 1

Overview of different welfare indicators related to pig sounds

Sounds	Indicators	Welfare	Production phase	Year
Coughs^［16］	Air quality	Environment	Weaners	2019
Coughs^［15］	Air quality	Environment	Weaners	2004
Coughs， sneezes^［17］	Air quality	Environment	Fattening	2022
Coughs^［28］	Respiratory disease	Health	Fattening	2001
Coughs^［29］	Respiratory disease	Health	Fattening	2008
Coughs^［30］	Respiratory disease	Health	Fattening	2008
Coughs^［31］	Respiratory disease	Health	Fattening	2008
Coughs^［32］	Respiratory disease	Health	Fattening	2008
Coughs^［33］	Respiratory disease	Health	Fattening	2008
Coughs^［34］	Respiratory disease	Health	Fattening	2008
Coughs^［35］	Wasting disease	Health	Fattening	2010
Coughs^［36］	Respiratory disease	Health	Fattening	2013
Coughs^［37］	Wasting disease	Health	Weaners	2013
Coughs^［38］	Wasting disease	Health	Weaners	2016
Coughs^［39］	Respiratory disease	Health	/	2020
Coughs^［40］	Wasting disease	Health	/	2020
Coughs^［41］	Respiratory disease	Health	Fattening	2021
Coughs^［42］	Respiratory disease	Health	Fattening	2021
Coughs， screams^［43］	Respiratory disease	Health	Farrowing	2016
Coughs， screams^［44］	Respiratory disease	Health	Farrowing	2020
Coughs^［45］	Respiratory disease	Health	/	2017
Coughs^［46］	Respiratory disease	Health	/	2019
Coughs^［47］	Respiratory disease	Health	/	2020
Coughs^［48］	Respiratory disease	Health	/	2020
Coughs^［49］	Respiratory disease	Health	Fattening	2019
Coughs^［50］	Respiratory disease	Health	Fattening	2019
Screams^［51］	Stress	Health	Piglets	2009
Screams^［27］	Stress	Physical condition	Piglets	2008
Screams^［21］	Stress	Physical condition	Piglets	2009
Screams^［22］	Stress	Physical condition	/	2012
Screams， grunts^［13］	Stress	Environment	Piglets	2013
Screams^［14］	Stress	Environment	Piglets	2020
Screams^［18］	Stress	Physical condition	/	2014
Screams， grunts^［23］	Stress	Physical condition	Piglets	2003
Screams^［25］	Stress	Physical condition	Fattening	2015
Screams^［52］	Distress	Physical condition	Farrowing， nursery， growing， and finishing	2018
Screams^［26］	Distress	Physical condition	Piglets	2016
Screams^［24］	Stress	Physical condition	Piglets	2018
Grunts， screams， howls^［19］	Distress	Physical condition	Farrowing	2020
Coughs， screams， howls^［20］	Distress	Physical condition	/	2017

Table 1

Fig. 1

Fig. 2

References 93

1	Development situation of pig industry in 2021 and prospect in 2022[J]. China Animal Industry, 2022(3): 39-40.
2	GAO G. Development prospect of China's meat industry in 2022[J]. Meat Industry, 2022(2): 1-5.
3	NORTON T, CHEN C, LARSEN M L V, et al. Review: Precision livestock farming: Building 'digital representations' to bring the animals closer to the farmer[J]. Animal, 2019, 13(12): 3009-3017.
4	GÓMEZ Y, STYGAR A H, BOUMANS I J M, et al. A systematic review on validated precision livestock farming technologies for pig production and its potential to assess animal welfare[J]. Frontiers in Veterinary Science, 2021, 8: ID 660565.
5	INGVARTSEN K L, NutritionMOYES K. Immune function and health of dairy cattle[J]. Animal, 2013, 7: 112-122.
6	DAWKINS M S. Animal welfare and efficient farming: Is conflict inevitable?[J]. Animal Production Science, 2017, 57(2): 201-208.
7	MAHFUZ S, MUN H S, DILAWAR M A, et al. Applications of smart technology as a sustainable strategy in modern swine farming[J]. Sustainability, 2022, 14(5): ID 2607.
8	TZANIDAKIS C, SIMITZIS P, ARVANITIS K, et al. An overview of the current trends in precision pig farming technologies[J]. Livestock Science, 2021, 249: ID 104530.
9	SCHILLINGS J, BENNETT R, ROSE D C. Exploring the potential of precision livestock farming technologies to help address farm animal welfare[J]. Frontiers in Animal Science, 2021, 2: ID 639678.
10	RACEWICZ P, LUDWICZAK A, SKRZYPCZAK E, et al. Welfare health and productivity in commercial pig herds[J]. Animals, 2021, 11(4): ID 1176.
11	MANTEUFFEL G, PUPPE B, SCHÖN P C. Vocalization of farm animals as a measure of welfare[J]. Applied Animal Behaviour Science, 2004, 88(1-2): 163-182.
12	MCLOUGHLIN M P, STEWART R, MCELLIGOTT A G. Automated bioacoustics: Methods in ecology and conservation and their potential for animal welfare monitoring[J]. Journal of The Royal Society Interface, 2019, 16(155): ID 20190225.
13	FERRARI S, COSTA A, GUARINO M. Heat stress assessment by swine related vocalizations[J]. Livestock Science, 2013, 151(1): 29-34.
14	AMARAL P I S, CAMPOS A T, YANAGI JUNIOR T, et al. Using sounds produced by pigs to identify thermoneutrality zones for thermal environment assessment ratios[J]. Engenharia Agrícola, 2020, 40(3): 266-271.
15	AVAN HIRTUM, BERCKMANS D. Objective recognition of cough sound as biomarker for aerial pollutants: Aerial pollutants and cough sound[J]. Indoor Air, 2004, 14(1): 10-15.
16	WANG X, ZHAO X, HE Y, et al. Cough sound analysis to assess air quality in commercial weaner barns[J]. Computers and Electronics in Agriculture, 2019, 160: 8-13.
17	PESSOA J, CAMP MONTORO J, PINA NUNES T, et al. Environmental risk factors influence the frequency of coughing and sneezing episodes in finisher pigs on a farm free of respiratory disease[J]. Animals, 2022, 12(8): ID 982.
18	MOI M, NÄÄS I DE A, CALDARA F R, et al. Vocalization data mining for estimating swine stress conditions[J]. Engenharia Agrícola, 2014, 34(3): 445-450.
19	CANG Y, LUO S, QIAO Y. Classification of pig sounds based on deep neural network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(9): 195-204.
20	ZHANG Z. Pig anomaly detection based on audio analysis technology[D]. Taiyuan: Taiyuan University of Technology, 2017.
21	LEIDIG M S, HERTRAMPF B, FAILING K, et al. Pain and discomfort in male piglets during surgical castration with and without local anaesthesia as determined by vocalisation and defence behaviour[J]. Applied Animal Behaviour Science, 2009, 116(2-4): 174-178.
22	CORDEIRO A F DA S, NÄÄS I DE A, OLIVEIRA S R DE M, et al. Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization[J]. Engenharia Agrícola, 2012, 32(2): 208-216.
23	MARX G, HORN T, THIELEBEIN J, et al. Analysis of pain-related vocalization in young pigs[J]. Journal of Sound and Vibration, 2003, 266(3): 687-698.
24	CORDEIRO A F DA S, NÄÄS I DE A, BARACHO M DOS S, et al. The use of vocalization signals to estimate the level of pain in piglets[J]. Engenharia Agrícola, 2018, 38(4): 486-490.
25	VANDERMEULEN J, BAHR C, TULLO E, et al. Discerning pig screams in production environments[J]. PLoS One, 2015, 10(4): ID e0123111.
26	RILEY J L, RILEY W D, CARROLL L M. Frequency characteristics in animal species typically used in laryngeal research: An exploratory investigation[J]. Journal of Voice, 2016, 30(6): e17-e24.
27	MOURA D J, SILVA W T, NAAS I A, et al. Real time computer stress monitoring of piglets using vocalization analysis[J]. Computers and Electronics in Agriculture, 2008, 64(1): 11-18.
28	MOSHOU D, CHEDAD A, AVAN HIRTUM, et al. Neural recognition system for swine cough[J]. Mathematics and Computers in Simulation, 2001, 56(4-5): 475-487.
29	FERRARI S, SILVA M, GUARINO M, et al. Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming[J]. Transactions of the ASABE, 2008, 51(3): 1051-1055.
30	SILVA M, FERRARI S, COSTA A, et al. Cough localization for the detection of respiratory diseases in pig houses[J]. Computers and Electronics in Agriculture, 2008, 64(2): 286-292.
31	FERRARI S, SILVA M, GUARINO M, et al. Cough sound analysis to identify respiratory infection in pigs[J]. Computers and Electronics in Agriculture, 2008, 64(2): 318-325.
32	EXADAKTYLOS V, SILVA M, FERRARI S, et al. Time-series analysis for online recognition and localization of sick pig (Sus scrofa) cough sounds[J]. The Journal of the Acoustical Society of America, 2008, 124(6): 3803-3809.
33	EXADAKTYLOS V, SILVA M, AERTS J M, et al. Real-time recognition of sick pig cough sounds[J]. Computers and Electronics in Agriculture, 2008, 63(2): 207-214.
34	GUARINO M, JANS P, COSTA A, et al. Field test of algorithm for automatic cough detection in pig houses[J]. Computers and Electronics in Agriculture, 2008, 62(1): 22-28.
35	GUTIERREZ W M, KIM S, KIM D H, et al. Classification of porcine wasting diseases using sound analysis[J]. Asian-Australasian Journal of Animal Sciences, 2010, 23(8): 1096-1104.
36	FERRARI S, SILVA M, EXADAKTYLOS V, et al. The sound makes the difference: The utility of real time sound analysis for health monitoring in pigs[M]// ALAND A, BANHAZI T. Livestock housing. The Netherlands: Wageningen Academic Publishers, 2013: 407-418.
37	CHUNG Y, OH S, LEE J, et al. Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems[J]. Sensors, 2013, 13(10): 12929-12942.
38	KIM H. Automatic identification of a coughing animal using audio and video data[C]// The fourth International Conference on Information Science and Cloud Computing — PoS(ISCC2015). Guangzhou, China: Sissa Medialab, 2016.
39	ZHAO J, LI X, LIU W, et al. DNN-HMM based acoustic model for continuous pig cough sound recognition[J]. International Journal of Agricultural and Biological Engineering, 2020, 13(3): 186-193.
40	HONG M, AHN H, ATIF O, et al. Field-applicable pig anomaly detection system using vocalization for embedded board implementations[J]. Applied Sciences, 2020, 10(19): ID 6991.
41	YIN Y, TU D, SHEN W, et al. Recognition of sick pig cough sounds based on convolutional neural network in field situations[J]. Information Processing in Agriculture, 2021, 8(3): 369-379.
42	SHEN W, TU D, YIN Y, et al. A new fusion feature based on convolutional neural network for pig cough recognition in field situations[J]. Information Processing in Agriculture, 2021, 8(4): 573-580.
43	XU, Y, SHEN M, YAN L, et al. Research of predelivery Meishan sow cough recognition algorithm[J]. Journal of Nanjing Agricultural University, 2016, 39(4): 681-687.
44	ZHANG H. Design and implementation of Meishan pig continuous cough sound monitoring system[D]. Nanjing: Nanjing Agricultural University, 2020.
45	ZHANG Z, TIAN J, WANG F, et al. The study on characteristic parameters extraction and recognition of pig cough sound[J]. Heilongjiang Animal Science and Veterinary Medicine, 2017(23): 18-22.
46	HAN L, TIAN J, ZHANG S, et al. Porcine abnormal sounds recognition using decision-tree-based support vector machine and fuzzy inference[J]. Animal Husbandry & Veterinary Medicine, 2019, 51(3): 38-44.
47	LI J, TIAN Y, ZHANG S. Research on recognition and localization of porcine cough sounds[J]. Heilongjiang Animal Science and Veterinary Medicine, 2020(14): 36-41.
48	ZHANG S. Research and application on multi-source monitoring and information fusion method for porcine abnormal behaviors[D]. Taiyuan: Taiyuan University of Technology, 2020.
49	ZHAO J. Pig cough sounds recognition based on deep learning[D]. Wuhan: Huazhong Agricultural University, 2019.
50	LI X, ZHAO J, GAO Y, et al. Pig continuous cough sound recognition based on continuous speech recognition technology[J]. Transactions of the CSAE, 2019, 35(6): 174-180.
51	RISI N, KÉSIA OLIVEIRA SILVA, PAULO R F Z, et al. Use of artificial intelligence to identify vocalizations emitted by sick and healthy piglets[C]// Livestock Environment VIII. St. Joseph, Michigan, USA: American Society of Agricultural and Biological Engineers, 2009.
52	CORDEIRO A F DA S, NÄÄS I DE A, SILVA LEITÃO FDA, et al. Use of vocalisation to identify sex, age, and distress in pig production[J]. Biosystems Engineering, 2018, 173: 57-63.
53	HALACHMI I, GUARINO M, BEWLEY J, et al. Smart animal agriculture: Application of real-time sensors to improve animal well-being and production[J]. Annual Review of Animal Biosciences, 2019, 7(1): 403-425.
54	BENJAMIN M, YIK S. Precision livestock farming in swine welfare: A review for swine practitioners[J]. Animals, 2019, 9(4): ID 133.
55	BERCKMANS D. General introduction to precision livestock farming[J]. Animal Frontiers, 2017, 7(1): 6-11.
56	POLITIS A, MESAROS A, ADAVANNE S, et al. Overview and evaluation of sound event localization and detection in DCASE 2019[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 29: 684-698.
57	CHANDRAKALA S, JAYALAKSHMI S L. Environmental audio scene and sound event recognition for autonomous surveillance: A survey and comparative studies[J]. ACM Computing Surveys, 2020, 52(3): 1-34.
58	ZEBARI R, ABDULAZEEZ A, ZEEBAREE D, et al. A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction[J]. Journal of Applied Science and Technology Trends, 2020, 1(2): 56-70.
59	GARRETA R, MONCECCHI G. Learning scikit-learn: machine learning in python: Experience the benefits of machine learning techniques by applying them to real-world problems using python and the open source scikit-learn library[M]. Birmingham, UK: Packt Publishing Ltd, 2013.
60	SHARMA G, UMAPATHY K, KRISHNAN S. Trends in audio signal feature extraction methods[J]. Applied Acoustics, 2020, 158: ID 107020.
61	SILVA CORDEIRO ADA, DE ALENCAR NÄÄS I, OLIVEIRA S, et al. Understanding vocalization might help to assess stressful conditions in piglets[J]. Animals, 2013, 3(3): 923-934.
62	DIANA A, CARPENTIER L, PIETTE D, et al. An ethogram of biter and bitten pigs during an ear biting event: First step in the development of a Precision Livestock Farming tool[J]. Applied Animal Behaviour Science, 2019, 215: 26-36.
63	ZHANG S, TIAN J, BANERJEE A, et al. Automatic recognition of porcine abnormalities based on a sound detection and recognition system[J]. Transactions of the ASABE, 2019, 62(6): 1755-1765.
64	DAVIS S, MERMELSTEIN P. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1980, 28(4): 357-366.
65	GIESERT A L, BALKE W T, JAHNS G. Probabilistic analysis of coughs in pigs to diagnose respiratory infections[J]. Landbauforschung vTI Agriculture and Forestry Research, 2011, 3(61): 237-242.
66	GENG Y, SONG P, LIN Y, et al. Voice recognition of abnormal state of pigs based on improved CNN[J]. Transactions of the CSAE, 2021, 37(20): 187-193.
67	LEE J, CHOI Y, PARK D, et al. Sound noise-robust porcine wasting diseases detection and classification system using convolutional neural network[J]. The Journal of Korean Institute of Information Technology, 2018, 16(5): 1-13.
68	THAKUR A, THAPAR D, RAJAN P, et al. Deep metric learning for bioacoustic classification: Overcoming training data scarcity using dynamic triplet loss[J]. The Journal of the Acoustical Society of America, 2019, 146(1): 534-547.
69	XIE J, HU K, ZHU M, et al. Investigation of different CNN-based models for improved bird sound classification[J]. IEEE Access, 2019, 7: 175353-175361.
70	FUZ, LUG, TINGK M, et al. A survey of audio-based music classification and annotation[J]. IEEE Transactions on Multimedia, 2011, 13(2): 303-319.
71	HUANG C J, CHEN Y J, CHEN H M, et al. Intelligent feature extraction and classification of anuran vocalizations[J]. Applied Soft Computing, 2014, 19: 1-7.
72	XIE J, HU K, ZHU M, et al. Bioacoustic signal classification in continuous recordings: Syllable-segmentation vs sliding-window[J]. Expert Systems with Applications, 2020, 152: ID 113390.
73	NGO D, HOANG H, NGUYEN A, et al. Sound context classification basing on join learning model and multi-Spectrogram features[J/OL]. arXiv:2005.12779 [cs, eess], 2020.
74	HUZAIFAH M. Comparison of time-frequency representations for environmental sound classification using convolutional neural networks[J/OL]. arXiv:1706.07156 [cs], 2017.
75	NGUYEN T, NGO D, PHAM L, et al. A re-trained model based on multi-kernel convolutional neural network for acoustic scene classification[C]// 2020 RIVF International Conference on Computing and Communication Technologies (RIVF). Piscataway, New York, USA: IEEE, 2020: 1-5.
76	DOUGLAS C E, MICHAEL F A. On distribution-free multiple comparisons in the one-way analysis of variance[J]. Communications in Statistics-Theory and Methods, 1991, 20(1): 127-139.
77	FERRARI S, SILVA M, SALA V, et al. Bioacoustics: A tool for diagnosis of respiratory pathologies in pig farms[J]. Journal of Agricultural Engineering, 2009, 40(1): ID 7.
78	SANCHEZ-VAZQUEZ M J, NIELEN M, EDWARDS S A, et al. Identifying associations between pig pathologies using a multi-dimensional machine learning methodology[J]. BMC Veterinary Research, 2012, 8(1): ID 151.
79	PAL N R, PAL K, KELLER J M, et al. A possibilistic fuzzy c-means clustering algorithm[J]. IEEE Transactions on Fuzzy Systems, 2005, 13(4): 517-530.
80	RTAYLI N, ENNEYA N. Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization[J]. Journal of Information Security and Applications, 2020, 55: ID 102596.
81	LEE J, JIN L, PARK D, et al. Acoustic features for pig wasting disease detection[J]. International Journal of Intellectual Property Management, 2015, 6(1): 37-46.
82	HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New York, USA: IEEE, 2016: 770-778.
83	LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
84	SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J/OL]. arXiv:1409.1556 [cs], 2015.
85	XIONG Z. Design of pig cough monitoring system in fattening pig houses[D]. Harbin: Harbin Engineering University, 2021.
86	HÄRDLE W K, SIMAR L. Applied multivariate statistical analysis[M]. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015.
87	VALLETTA J J, TORNEY C, KINGS M, et al. Applications of machine learning in animal behaviour studies[J]. Animal Behaviour, 2017, 124: 203-220.
88	EXADAKTYLOS V, SILVA M, FERRARI S, et al. Sound localisation in practice: An application in localisation of sick animals in commercial piggeries[M]// STRUMILLO P. Advances in sound localization. Shenzhen: InTech, 2011.
89	WANG R. How do pig practitioners consider artificial intelligence in pig farming?[J]. Swine Industry Science, 2019, 36(4): 46-48.
90	SILVAA P S P, STORINOG Y, FERREYRAF S M, et al. Cough associated with the detection of Mycoplasma hyopneumoniae DNA in clinical and environmental specimens under controlled conditions[J]. Porcine Health Management, 2022, 8(1): ID 6.
91	PESSOA J, RODRIGUES DA COSTA M, GARCÍA MANZANILLA E, et al. Managing respiratory disease in finisher pigs: Combining quantitative assessments of clinical signs and the prevalence of lung lesions at slaughter[J]. Preventive Veterinary Medicine, 2021, 186: ID 105208.
92	SCHÖN P, PUPPE B, MANTEUFFEL G. Automated recording of stress vocalisations as a tool to document impaired welfare in pigs[J]. Animal Welfare, 2004, 13: 105-110.
93	SUN J. Technology changes pig farming[J]. China Rural Science & Technology, 2020(1): 36-39.

[1]	KANG Xi, LIU Gang, CHU Mengyuan, LI Qian, WANG Yanchao. Advances and Challenges in Physiological Parameters Monitoring and Diseases Diagnosing of Dairy Cows Based on Computer Vision [J]. Smart Agriculture, 2022, 4(2): 1-18.
[2]	HAN Shuqing, ZHANG Jing, CHENG Guodong, PENG Yingqi, ZHANG Jianhua, WU Jianzhai. Current State and Challenges of Automatic Lameness Detection in Dairy Cattle [J]. Smart Agriculture, 2020, 2(3): 21-36.
[3]	Fu Yuanyuan, Yang Guijun, Duan Dandan, Zhang Yongtao, Gu Xiaohe, Yang Xiaodong, Xu Xingang, Li Zhenhai. Comparison analysis of spatial and spectral feature in vegetation classification based on AVIRIS hyperspectral image [J]. Smart Agriculture, 2020, 2(1): 68-76.
[4]	Zhao Yiguang, Yang Liang, Zheng Shanshan, Xiong Benhai. Advances in the development and applications of intelligent equipment and feeding technology for livestock production [J]. Smart Agriculture, 2019, 1(1): 20-31.