AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Smart Agriculture Article
PDF (688.8 KB)
Cite
EndNote(RIS) BibTeX
Collect
AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Pig Sound Analysis: A Measure of Welfare

Nan Ji, Yanling Yin, Weizheng Shen( ), Shengli Kou, Baisheng Dai, Guowei Wang
College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China
Show Author Information

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.

Keywords

feature extractionanimal welfarepig sound classificationsound analysisprecision livestock farmingsound monitering

References

[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.
Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[5]

INGVARTSEN K L, NutritionMOYES K. Immune function and health of dairy cattle[J]. Animal, 2013, 7: 112-122.
Crossref Google Scholar
[6]

DAWKINS M S. Animal welfare and efficient farming: Is conflict inevitable?[J]. Animal Production Science, 2017, 57(2): 201-208.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[10]

RACEWICZ P, LUDWICZAK A, SKRZYPCZAK E, et al. Welfare health and productivity in commercial pig herds[J]. Animals, 2021, 11(4): ID 1176.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[13]

FERRARI S, COSTA A, GUARINO M. Heat stress assessment by swine related vocalizations[J]. Livestock Science, 2013, 151(1): 29-34.
Crossref Google Scholar
[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.
Crossref Google Scholar
[15]

HIRTUM AVAN, 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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[25]

VANDERMEULEN J, BAHR C, TULLO E, et al. Discerning pig screams in production environments[J]. PLoS One, 2015, 10(4): ID e0123111.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[28]

MOSHOU D, CHEDAD A, HIRTUM AVAN, et al. Neural recognition system for swine cough[J]. Mathematics and Computers in Simulation, 2001, 56(4-5): 475-487.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref
[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.
Crossref Google Scholar
[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.
Crossref
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Google Scholar
[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.
Google Scholar
[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.
Google Scholar
[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.
Google Scholar
[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.
Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[54]

BENJAMIN M, YIK S. Precision livestock farming in swine welfare: A review for swine practitioners[J]. Animals, 2019, 9(4): ID 133.
Crossref Google Scholar
[55]

BERCKMANS D. General introduction to precision livestock farming[J]. Animal Frontiers, 2017, 7(1): 6-11.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Google Scholar
[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.
Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Google Scholar
[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.
Crossref
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref
[89]

WANG R. How do pig practitioners consider artificial intelligence in pig farming?[J]. Swine Industry Science, 2019, 36(4): 46-48.
Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[93]

SUN J. Technology changes pig farming[J]. China Rural Science & Technology, 2020(1): 36-39.
Google Scholar
Smart Agriculture
Volume 4 Issue 1-4,
2022
Pages 19-35
DOI: 10.12133/j.smartag.SA202204004
Cite this article:
Ji N, Yin Y, Shen W, et al. Pig Sound Analysis: A Measure of Welfare. Smart Agriculture, 2022, 4(2): 19-35. https://doi.org/10.12133/j.smartag.SA202204004

625

Views

86

Downloads

0

Crossref

4

Scopus

Altmetrics
Received: 24 April 2022
Published: 30 June 2022
© The Author(s) 2022.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Return