{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T21:08:33Z","timestamp":1789160913727,"version":"build-2803163510"},"reference-count":135,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T00:00:00Z","timestamp":1768694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>Respiratory diseases remain one of the most significant health challenges in modern swine production, leading to substantial economic losses, compromised animal welfare, and increased antimicrobial use. In recent years, advances in artificial intelligence (AI), particularly machine learning and deep learning, have enabled the development of non-invasive, continuous monitoring systems based on pig vocalizations. Among these, audio-based technologies have emerged as especially promising tools for early detection and monitoring of respiratory disorders under real farm conditions. This review provides a comprehensive synthesis of AI-driven audio classification approaches applied to pig farming, with focus on respiratory health and welfare monitoring. First, the biological and acoustic foundations of pig vocalizations and their relevance to health and welfare assessment are outlined. The review then systematically examines sound acquisition technologies, feature engineering strategies, machine learning and deep learning models, and evaluation methodologies reported in the literature. Commercially available systems and recent advances in real-time, edge, and on-farm deployment are also discussed. Finally, key challenges related to data scarcity, generalization, environmental noise, and practical deployment are identified, and emerging opportunities for future research including multimodal sensing, standardized datasets, and explainable AI are highlighted. This review aims to provide researchers, engineers, and industry stakeholders with a consolidated reference to guide the development and adoption of robust AI-based acoustic monitoring systems for respiratory health management in swine.<\/jats:p>","DOI":"10.3390\/biology15020177","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:23:56Z","timestamp":1768811036000},"page":"177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Advances in Audio Classification and Artificial Intelligence for Respiratory Health and Welfare Monitoring in Swine"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3195-2891","authenticated-orcid":false,"given":"Md","family":"Sharifuzzaman","sequence":"first","affiliation":[{"name":"Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon-si 57922, Republic of Korea"},{"name":"Department of Animal Science and Veterinary Medicine, Gopalganj Science and Technology University, Gopalganj 8105, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0322-6462","authenticated-orcid":false,"given":"Hong-Seok","family":"Mun","sequence":"additional","affiliation":[{"name":"Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon-si 57922, Republic of Korea"},{"name":"Department of Multimedia Engineering, Sunchon National University, Suncheon-si 57922, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0344-1680","authenticated-orcid":false,"given":"Eddiemar B.","family":"Lagua","sequence":"additional","affiliation":[{"name":"Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon-si 57922, Republic of Korea"},{"name":"Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, Suncheon-si 57922, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Kamrul","family":"Hasan","sequence":"additional","affiliation":[{"name":"Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon-si 57922, Republic of Korea"},{"name":"Department of Poultry Science, Sylhet Agricultural University, Sylhet 3100, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin-Gu","family":"Kang","sequence":"additional","affiliation":[{"name":"Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon-si 57922, Republic of Korea"},{"name":"Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, Suncheon-si 57922, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young-Hwa","family":"Kim","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Chonnam National University, Gwangju-si 61186, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7466-4173","authenticated-orcid":false,"given":"Ahsan","family":"Mehtab","sequence":"additional","affiliation":[{"name":"Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon-si 57922, Republic of Korea"},{"name":"School Education Department, Narowal 51600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hae-Rang","family":"Park","sequence":"additional","affiliation":[{"name":"Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon-si 57922, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chul-Ju","family":"Yang","sequence":"additional","affiliation":[{"name":"Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon-si 57922, Republic of Korea"},{"name":"Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, Suncheon-si 57922, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104530","DOI":"10.1016\/j.livsci.2021.104530","article-title":"An Overview of the Current Trends in Precision Pig Farming Technologies","volume":"249","author":"Tzanidakis","year":"2021","journal-title":"Livest. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mari\u0107, K., Gvozdanovi\u0107, K., Djurkin Ku\u0161ec, I., Ku\u0161ec, G., and Margeta, V. (2025). Smart Pig Farms: Integration and Application of Digital Technologies in Pig Production. Agriculture, 15.","DOI":"10.3390\/agriculture15090937"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Racewicz, P., Ludwiczak, A., Skrzypczak, E., Sk\u0142adanowska-Baryza, J., Biesiada, H., Nowak, T., Nowaczewski, S., Zaborowicz, M., Stanisz, M., and \u015al\u00f3sarz, P. (2021). Welfare Health and Productivity in Commercial Pig Herds. Animals, 11.","DOI":"10.3390\/ani11041176"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, C.-S., and Chen, W.-C. (2019). Research and Development of Automatic Monitoring System for Livestock Farms. Appl. Sci., 9.","DOI":"10.3390\/app9061132"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"17582","DOI":"10.1038\/s41598-017-17451-6","article-title":"Automated Tracking to Measure Behavioural Changes in Pigs for Health and Welfare Monitoring","volume":"7","author":"Matthews","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pandey, S., Kalwa, U., Kong, T., Guo, B., Gauger, P.C., Peters, D.J., and Yoon, K.-J. (2021). Behavioral Monitoring Tool for Pig Farmers: Ear Tag Sensors, Machine Intelligence, and Technology Adoption Roadmap. Animals, 11.","DOI":"10.3390\/ani11092665"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3604806","article-title":"PigSense: Structural Vibration-Based Activity and Health Monitoring System for Pigs","volume":"20","author":"Dong","year":"2023","journal-title":"ACM Trans. Sen. Netw."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Benjamin, M., and Yik, S. (2019). Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners. Animals, 9.","DOI":"10.3390\/ani9040133"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108009","DOI":"10.1016\/j.compag.2023.108009","article-title":"Enhanced Camera-Based Individual Pig Detection and Tracking for Smart Pig Farms","volume":"211","author":"Guo","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"277","DOI":"10.5187\/jast.2024.e113","article-title":"A Review of Sound-Based Pig Monitoring for Enhanced Precision Production","volume":"67","author":"Reza","year":"2025","journal-title":"J. Anim. Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, S., Jiang, H., Qiao, Y., Jiang, S., Lin, H., and Sun, Q. (2022). The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming. Sensors, 22.","DOI":"10.3390\/s22176541"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"805.e1","DOI":"10.1016\/j.jvoice.2020.01.013","article-title":"Effect of Ventricular Folds on Vocalization Fundamental Frequency in Domestic Pigs (Sus scrofa domesticus)","volume":"35","author":"Herbst","year":"2021","journal-title":"J. Voice"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2895","DOI":"10.1121\/1.5100612","article-title":"Expression of Emotional Valence in Pig Closed-Mouth Grunts: Involvement of Both Source- and Filter-Related Parameters","volume":"145","author":"Briefer","year":"2019","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1111\/j.1469-7998.2009.00661.x","article-title":"The Contribution of Source\u2013Filter Theory to Mammal Vocal Communication Research","volume":"280","author":"Taylor","year":"2010","journal-title":"J. Zool."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Linhart, P., Ratcliffe, V.F., Reby, D., and \u0160pinka, M. (2015). Expression of Emotional Arousal in Two Different Piglet Call Types. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0135414"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.physbeh.2017.09.010","article-title":"Vocal Correlates of Emotional Reactivity within and across Contexts in Domestic Pigs (Sus scrofa)","volume":"181","author":"Leliveld","year":"2017","journal-title":"Physiol. Behav."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"eadi8081","DOI":"10.1126\/science.adi8081","article-title":"Brainstem Control of Vocalization and Its Coordination with Respiration","volume":"383","author":"Park","year":"2024","journal-title":"Science"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Villain, A.S., Lanthony, M., Gu\u00e9rin, C., and Tallet, C. (2020). Manipulable Object and Human Contact: Preference and Modulation of Emotional States in Weaned Pigs. Front. Vet. Sci., 7.","DOI":"10.3389\/fvets.2020.577433"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e36","DOI":"10.24072\/pcjournal.260","article-title":"The Use of Pigs Vocalisation Structure to Assess the Quality of Human-Pig Relationship","volume":"3","author":"Villain","year":"2023","journal-title":"Peer Community J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2062","DOI":"10.1038\/s41598-019-38514-w","article-title":"Positive and Negative Contexts Predict Duration of Pig Vocalisations","volume":"9","author":"Friel","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"18824","DOI":"10.1038\/s41598-023-45242-9","article-title":"Author Correction: Classification of Pig Calls Produced from Birth to Slaughter According to Their Emotional Valence and Context of Production","volume":"13","author":"Briefer","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dalmau, A., S\u00e1nchez-Matamoros, A., Molina, J.M., Xercavins, A., Varvar\u00f3-Porter, A., Mu\u00f1oz, I., Moles, X., Baulida, B., F\u00e0brega, E., and Velarde, A. (2021). Intramuscular vs. Intradermic Needle-Free Vaccination in Piglets: Relevance for Animal Welfare Based on an Aversion Learning Test and Vocalizations. Front. Vet. Sci., 8.","DOI":"10.3389\/fvets.2021.715260"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"18496","DOI":"10.1038\/s41598-020-75378-x","article-title":"Piglets Vocally Express the Anticipation of Pseudo-Social Contexts in Their Grunts","volume":"10","author":"Villain","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hou, Y., Li, Q., Wang, Z., Liu, T., He, Y., Li, H., Ren, Z., Guo, X., Yang, G., and Liu, Y. (2024). Study on a Pig Vocalization Classification Method Based on Multi-Feature Fusion. Sensors, 24.","DOI":"10.3390\/s24020313"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Olczak, K., Penar, W., Nowicki, J., Magiera, A., and Klocek, C. (2023). The Role of Sound in Livestock Farming\u2014Selected Aspects. Animals, 13.","DOI":"10.3390\/ani13142307"},{"key":"ref_26","first-page":"186","article-title":"DNN-HMM Based Acoustic Model for Continuous Pig Cough Sound Recognition","volume":"13","author":"Zhao","year":"2020","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.biosystemseng.2024.01.002","article-title":"Automatic Detection of Continuous Pig Cough in a Complex Piggery Environment","volume":"238","author":"Wang","year":"2024","journal-title":"Biosyst. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106994","DOI":"10.1016\/j.compag.2022.106994","article-title":"Fusion of Acoustic and Deep Features for Pig Cough Sound Recognition","volume":"197","author":"Shen","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107645","DOI":"10.1016\/j.compag.2023.107645","article-title":"An Investigation of Fusion Strategies for Boosting Pig Cough Sound Recognition","volume":"205","author":"Yin","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pan, W., Li, H., Zhou, X., Jiao, J., Zhu, C., and Zhang, Q. (2024). Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models. Sensors, 24.","DOI":"10.3390\/s24041269"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"12929","DOI":"10.3390\/s131012929","article-title":"Automatic Detection and Recognition of Pig Wasting Diseases Using Sound Data in Audio Surveillance Systems","volume":"13","author":"Chung","year":"2013","journal-title":"Sensors"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compag.2019.03.001","article-title":"Cough Sound Analysis to Assess Air Quality in Commercial Weaner Barns","volume":"160","author":"Wang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.compag.2008.07.003","article-title":"Cough Sound Analysis to Identify Respiratory Infection in Pigs","volume":"64","author":"Ferrari","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13028-020-00529-z","article-title":"A Descriptive Study of Acute Outbreaks of Respiratory Disease in Norwegian Fattening Pig Herds","volume":"62","author":"Cohen","year":"2020","journal-title":"Acta Vet. Scand."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s40813-017-0065-2","article-title":"Etiology of Acute Respiratory Disease in Fattening Pigs in Finland","volume":"3","author":"Laurila","year":"2017","journal-title":"Porc. Health Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"105789","DOI":"10.1016\/j.compag.2020.105789","article-title":"Frequency Analysis of the Sneeze Caused by Swine Influenza Virus Strains: Automatic Sneeze around-the-Clock Detection Using a Support Vector Machine","volume":"179","author":"Mito","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105264","DOI":"10.1016\/j.applanim.2021.105264","article-title":"Vocalisations in Farm Animals: A Step towards Positive Welfare Assessment","volume":"236","author":"Laurijs","year":"2021","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Vandermeulen, J., Bahr, C., Tullo, E., Fontana, I., Ott, S., Kashiha, M., Guarino, M., Moons, C.P.H., Tuyttens, F.a.M., and Niewold, T.A. (2015). Discerning Pig Screams in Production Environments. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0123111"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4523","DOI":"10.1038\/s41598-024-55336-7","article-title":"Detecting Tail Biters by Monitoring Pig Screams in Weaning Pigs","volume":"14","author":"Heseker","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wu, X., Zhou, S., Chen, M., Zhao, Y., Wang, Y., Zhao, X., Li, D., and Pu, H. (2022). Combined Spectral and Speech Features for Pig Speech Recognition. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0276778"},{"key":"ref_41","first-page":"4907","article-title":"Domestic Pig Sound Classification Based on TransformerCNN","volume":"53","author":"Liao","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2994","DOI":"10.1038\/s41598-023-30057-5","article-title":"Spectro-Temporal Acoustic Elements of Music Interact in an Integrated Way to Modulate Emotional Responses in Pigs","volume":"13","author":"Ceballos","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Alvarez-Hernandez, N., Vallejo-Timar\u00e1n, D., and de Jes\u00fas Rodriguez, B. (2023). Adapted Original Music as an Environmental Enrichment in an Intensive Pig Production System Reduced Aggression in Weaned Pigs during Regrouping. Animals, 13.","DOI":"10.3390\/ani13233599"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"434","DOI":"10.5965\/223811711942020434","article-title":"Evaluating the Influence of Noise Emitted by Environmental Enrichment Equipment in Routine Farrowing Management Practices","volume":"19","author":"Ricci","year":"2020","journal-title":"Rev. Ci\u00eancias Agroveterin\u00e1rias"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1590\/1807-1929\/agriambi.v23n3p196-202","article-title":"Welfare and Spatial Distribution of Noise Levels in Swine Nursery","volume":"23","author":"Damasceno","year":"2019","journal-title":"Rev. Bras. Eng. Agr\u00edcla Ambient."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103847","DOI":"10.1016\/j.livsci.2019.103847","article-title":"Status Quo Analysis of Noise Levels in Pig Fattening Units in Germany","volume":"230","author":"Wegner","year":"2019","journal-title":"Livest. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hong, M., Ahn, H., Atif, O., Lee, J., Park, D., and Chung, Y. (2020). Field-Applicable Pig Anomaly Detection System Using Vocalization for Embedded Board Implementations. Appl. Sci., 10.","DOI":"10.3390\/app10196991"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"105410","DOI":"10.1016\/j.livsci.2024.105410","article-title":"Determination of the Sound Level during Different Management Measures in Piglet Rearing Related to Animal Welfare and Human Health and Safety","volume":"280","author":"Kroll","year":"2024","journal-title":"Livest. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Pann, V., Kwon, K., Kim, B., Jang, D.-H., and Kim, J.-B. (2024). DCNN for Pig Vocalization and Non-Vocalization Classification: Evaluate Model Robustness with New Data. Animals, 14.","DOI":"10.3390\/ani14142029"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1300\/J096v10n01_04","article-title":"Occupational Noise Exposure Assessment in Intensive Swine Farrowing Systems: Dosimetry, Octave Band, and Specific Task Analysis","volume":"10","author":"Humann","year":"2005","journal-title":"J. Agromedicine"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1006\/jaer.1998.0291","article-title":"The Acoustic Environment of the Domestic Pig","volume":"71","author":"Talling","year":"1998","journal-title":"J. Agric. Eng. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"103770","DOI":"10.1016\/j.ipm.2024.103770","article-title":"Sound Identification of Abnormal Pig Vocalizations: Enhancing Livestock Welfare Monitoring on Smart Farms","volume":"61","author":"Xie","year":"2024","journal-title":"Inf. Process. Manag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s43926-023-00049-y","article-title":"Comparative Analysis of Audio Classification with MFCC and STFT Features Using Machine Learning Techniques","volume":"4","author":"Gourisaria","year":"2024","journal-title":"Discov. Internet Things"},{"key":"ref_54","first-page":"1651560","article-title":"Music Feature Extraction and Classification Algorithm Based on Deep Learning","volume":"2021","author":"Zhang","year":"2021","journal-title":"Sci. Program."},{"key":"ref_55","first-page":"678","article-title":"Cross-Domain Synergy: Leveraging Image Processing Techniques for Enhanced Sound Classification through Spectrogram Analysis Using CNNs","volume":"6","author":"Franzoni","year":"2023","journal-title":"J. Auton. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Singh, J. (2022, January 11\u201317). pyAudioProcessing: Audio Processing, Feature Extraction, and Machine Learning Modeling. Proceedings of the 21st Python in Science Conference (SciPy 2022), Austin, TX, USA.","DOI":"10.25080\/majora-212e5952-02e"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"106620","DOI":"10.1109\/ACCESS.2023.3318015","article-title":"A Survey of Audio Classification Using Deep Learning","volume":"11","author":"Zaman","year":"2023","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"107020","DOI":"10.1016\/j.apacoust.2019.107020","article-title":"Trends in Audio Signal Feature Extraction Methods","volume":"158","author":"Sharma","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Melo, D.D.F.P., Fadigas, I.D.S., and Pereira, H.B.D.B. (2020). Graph-Based Feature Extraction: A New Proposal to Study the Classification of Music Signals Outside the Time-Frequency Domain. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0240915"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Jin, W., Wang, X., and Zhan, Y. (2022). Environmental Sound Classification Algorithm Based on Region Joint Signal Analysis Feature and Boosting Ensemble Learning. Electronics, 11.","DOI":"10.3390\/electronics11223743"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/JSTSP.2019.2908700","article-title":"Deep Learning for Audio Signal Processing","volume":"13","author":"Purwins","year":"2019","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1186\/s13634-022-00933-9","article-title":"Classification of Audio Signals Using Spectrogram Surfaces and Extrinsic Distortion Measures","volume":"2022","author":"Levy","year":"2022","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Pan, W., Jiao, J., Zhou, X., Xu, Z., Gu, L., and Zhu, C. (2024). Underdetermined Blind Source Separation of Audio Signals for Group Reared Pigs Based on Sparse Component Analysis. Sensors, 24.","DOI":"10.3390\/s24165173"},{"key":"ref_64","first-page":"16","article-title":"Analysis of Livestock Vocal Data using Lightweight MobileNet","volume":"13","author":"Chung","year":"2024","journal-title":"Smart Media J."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"34703","DOI":"10.1038\/s41598-025-17205-9","article-title":"A Multi-Stage Ensemble Framework for Classifying Pig Vocalizations under Noisy Animal Farm Environments","volume":"15","author":"Chung","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_66","first-page":"19","article-title":"Pig Sound Analysis: A Measure of Welfare","volume":"4","author":"Ji","year":"2022","journal-title":"Smart Agric."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Wen, T.C., Freire, C.F., Benicio, L.M., de Moura, G.B., Amorim, M.D., and da Silva-Miranda, K.O. (2025). TinyML-Based Swine Vocalization Pattern Recognition for Enhancing Animal Welfare in Embedded Systems. Inventions, 10.","DOI":"10.3390\/inventions10040052"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Kaur, J., Hazrati Fard, S.M., Amiri-Zarandi, M., and Dara, R. (2022). Protecting Farmers\u2019 Data Privacy and Confidentiality: Recommendations and Considerations. Front. Sustain. Food Syst., 6.","DOI":"10.3389\/fsufs.2022.903230"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"e9955","DOI":"10.7717\/peerj.9955","article-title":"High Microphone Signal-to-Noise Ratio Enhances Acoustic Sampling of Wildlife","volume":"8","author":"Darras","year":"2020","journal-title":"PeerJ"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"van Erp-van der Kooij, E., de Graaf, L.F., de Kruijff, D.A., Pellegrom, D., de Rooij, R., Welters, N.I.T., and van Poppel, J. (2023). Using Sound Location to Monitor Farrowing in Sows. Animals, 13.","DOI":"10.3390\/ani13223538"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1111\/2041-210X.14390","article-title":"Spatial Ecosystem Monitoring with a Multichannel Acoustic Autonomous Recording Unit (MAARU)","volume":"15","author":"Heath","year":"2024","journal-title":"Methods Ecol. Evol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1111\/j.2041-210X.2012.00209.x","article-title":"Field Test of an Affordable, Portable, Wireless Microphone Array for Spatial Monitoring of Animal Ecology and Behaviour","volume":"3","author":"Mennill","year":"2012","journal-title":"Methods Ecol. Evol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"6794","DOI":"10.1002\/ece3.6216","article-title":"Acoustic Localization of Terrestrial Wildlife: Current Practices and Future Opportunities","volume":"10","author":"Rhinehart","year":"2020","journal-title":"Ecol. Evol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"101081","DOI":"10.1016\/j.crmeth.2025.101081","article-title":"Mountable Miniature Microphones to Identify and Assign Mouse Ultrasonic Vocalizations","volume":"5","author":"Waidmann","year":"2025","journal-title":"Cell Rep. Methods"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"G\u00f3mez, Y., Stygar, A.H., Boumans, I.J.M.M., Bokkers, E.A.M., Pedersen, L.J., Niemi, J.K., Pastell, M., Manteca, X., and Llonch, P. (2021). A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare. Front. Vet. Sci., 8.","DOI":"10.3389\/fvets.2021.660565"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1111\/2041-210X.12610","article-title":"A Minimum-Impact, Flexible Tool to Study Vocal Communication of Small Animals with Precise Individual-Level Resolution","volume":"7","author":"Gill","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Garrido, L.F.C., Rodrigues, G.S.T., Costa, L.B., Kurtz, D.J., and Daros, R.R. (2025). Validation of a Swine Cough Monitoring System Under Field Conditions. Agriengineering, 7.","DOI":"10.3390\/agriengineering7050140"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Lagua, E.B., Mun, H.-S., Ampode, K.M.B., Chem, V., Kim, Y.-H., and Yang, C.-J. (2023). Artificial Intelligence for Automatic Monitoring of Respiratory Health Conditions in Smart Swine Farming. Animals, 13.","DOI":"10.3390\/ani13111860"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Eddicks, M., Feicht, F., Beckjunker, J., Genzow, M., Alonso, C., Reese, S., Ritzmann, M., and Stadler, J. (2024). Monitoring of Respiratory Disease Patterns in a Multimicrobially Infected Pig Population Using Artificial Intelligence and Aggregate Samples. Viruses, 16.","DOI":"10.3390\/v16101575"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"2858740","DOI":"10.1155\/2019\/2858740","article-title":"An Efficient Porcine Acoustic Signal Denoising Technique Based on EEMD-ICA-WTD","volume":"2019","author":"Zhang","year":"2019","journal-title":"Math. Probl. Eng."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"109975","DOI":"10.1016\/j.compag.2025.109975","article-title":"Research on Improved Sound Recognition Model for Oestrus Detection in Sows","volume":"231","author":"Cao","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Barnhill, A., Noeth, E., Maier, A., and Bergler, C. (2024, January 1\u20135). ANIMAL-CLEAN\u2014A Deep Denoising Toolkit for Animal-Independent Signal Enhancement. Proceedings of the Interspeech 2024, Kos, Greece.","DOI":"10.21437\/Interspeech.2024-1151"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"199","DOI":"10.33545\/27076571.2024.v5.i2c.114","article-title":"Deep Learning Approaches for Acoustic Animal Classification","volume":"5","author":"Daneshyari","year":"2024","journal-title":"Int. J. Comput. Artif. Intell."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"012085","DOI":"10.1088\/1742-6596\/1453\/1\/012085","article-title":"A Comparison on Data Augmentation Methods Based on Deep Learning for Audio Classification","volume":"1453","author":"Wei","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"5338","DOI":"10.21105\/joss.05338","article-title":"Crowsetta: A Python Tool to Work with Any Format for Annotating Animal Vocalizations and Bioacoustics Data","volume":"8","author":"Nicholson","year":"2023","journal-title":"J. Open Source Softw."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"e15913","DOI":"10.7717\/peerj.15913","article-title":"NEAL: An Open-Source Tool for Audio Annotation","volume":"11","author":"Gibbons","year":"2023","journal-title":"PeerJ"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Colligan, T., Irish, K., Emlen, D.J., and Wheeler, T.J. (2023). DISCO: A Deep Learning Ensemble for Uncertainty-Aware Segmentation of Acoustic Signals. PLoS ONE, 18.","DOI":"10.1101\/2023.01.24.525459"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.12688\/f1000research.26369.3","article-title":"ecoSound-Web: An Open-Source, Online Platform for Ecoacoustics","volume":"9","author":"Darras","year":"2024","journal-title":"F1000Research"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"102043","DOI":"10.1016\/j.ecoinf.2023.102043","article-title":"An Integrated System for the Acoustic Monitoring of Goat Farms","volume":"75","author":"Ntalampiras","year":"2023","journal-title":"Ecol. Inform."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"104811","DOI":"10.1016\/j.livsci.2021.104811","article-title":"A Monophonic Cow Sound Annotation Tool Using a Semi-Automatic Method on Audio\/Video Data","volume":"256","author":"Pandeya","year":"2022","journal-title":"Livest. Sci."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1111\/2041-210X.14468","article-title":"Whombat: An Open-Source Audio Annotation Tool for Machine Learning Assisted Bioacoustics","volume":"16","author":"Weldy","year":"2025","journal-title":"Methods Ecol. Evol."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Manikandan, V., and Neethirajan, S. (2025). AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring. Sensors, 25.","DOI":"10.3390\/s25134058"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Teterja, D., Garcia-Rodriguez, J., Azorin-Lopez, J., Sebastian-Gonzalez, E., Nedi\u0107, D., Lekovi\u0107, D., Kne\u017eevi\u0107, P., Draji\u0107, D., and Vukobratovi\u0107, D. (2024). A Video Mosaicing-Based Sensing Method for Chicken Behavior Recognition on Edge Computing Devices. Sensors, 24.","DOI":"10.3390\/s24113409"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Semenov, S., Karlov, D., Solecki, M., Ruban, I., Kovalenko, A., and Piskarov, O. (2025). Integrated Model for Intelligent Monitoring and Diagnostics of Animal Health Based on IoT Technology for the Digital Farm. Sustainability, 17.","DOI":"10.3390\/su17188507"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"105286","DOI":"10.1016\/j.compag.2020.105286","article-title":"Machine Learning Based Fog Computing Assisted Data-Driven Approach for Early Lameness Detection in Dairy Cattle","volume":"171","author":"Taneja","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"8199","DOI":"10.1109\/JIOT.2023.3318298","article-title":"Lightweight LAE for Anomaly Detection With Sound-Based Architecture in Smart Poultry Farm","volume":"11","author":"Goyal","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Kim, J., Suh, Y., Lee, J., Chae, H., Ahn, H., Chung, Y., and Park, D. (2022). EmbeddedPigCount: Pig Counting with Video Object Detection and Tracking on an Embedded Board. Sensors, 22.","DOI":"10.3390\/s22072689"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Ngo, H.Q.T., Nguyen, T.P., and Nguyen, H. (2020). Research on a Low-Cost, Open-Source, and Remote Monitoring Data Collector to Predict Livestock\u2019s Habits Based on Location and Auditory Information: A Case Study from Vietnam. Agriculture, 10.","DOI":"10.3390\/agriculture10050180"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Alzuhair, A., and Alghaihab, A. (2023). The Design and Optimization of an Acoustic and Ambient Sensing AIoT Platform for Agricultural Applications. Sensors, 23.","DOI":"10.3390\/s23146262"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Song, H., Zhao, B., Hu, J., Sun, H., and Zhou, Z. (2022). Research on Improved DenseNets Pig Cough Sound Recognition Model Based on SENets. Electronics, 11.","DOI":"10.3390\/electronics11213562"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"155","DOI":"10.53106\/199115992022123306013","article-title":"An Improved Machine Learning Model for Pig Abnormal Voice Recognition","volume":"33","author":"Shi","year":"2022","journal-title":"J. Comput."},{"key":"ref_102","first-page":"413","article-title":"Research and Application of End-to-End Pig Speech Recognition Model","volume":"Volume 12615","author":"Du","year":"2022","journal-title":"Proceedings of the International Conference on Signal Processing and Communication Technology (SPCT 2022)"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"2354021","DOI":"10.1142\/S0218001423540216","article-title":"A CNN-SVM Study Based on the Fusion of Spectrogram and Thermal Imaging Heterogeneous Features for Pig Cough Recognition in Field Situation","volume":"37","author":"Wang","year":"2023","journal-title":"Int. J. Patt. Recogn. Artif. Intell."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.13031\/trans.12975","article-title":"Automatic Recognition of Porcine Abnormalities Based on a Sound Detection and Recognition System","volume":"62","author":"Zhang","year":"2019","journal-title":"Trans. Asabe"},{"key":"ref_105","first-page":"369","article-title":"Recognition of Sick Pig Cough Sounds Based on Convolutional Neural Network in Field Situations","volume":"8","author":"Yin","year":"2021","journal-title":"Inf. Process. Agric."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"101863","DOI":"10.1016\/j.ecoinf.2022.101863","article-title":"A Lightweight CNN-Based Model for Early Warning in Sow Oestrus Sound Monitoring","volume":"72","author":"Wang","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_107","unstructured":"Peng, S., Zha, W., Chen, C., Tang, X., Wu, G., Gu, L., and Jiao, J. (2025, December 10). Pig Audio Recognition Based on Deep Neural Network (DNN) and Hidden Markov Models (HMM), SSRN Electron. J., Available online: https:\/\/ssrn.com\/abstract=4312454."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Wang, B., Miao, X., Jiang, M., Yin, C., and Pu, C. (2022, January 11\u201312). Application of Deep Learning Algorithm Based on Neural Network. Proceedings of the 2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS), Dalian, China.","DOI":"10.1109\/TOCS56154.2022.10016118"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Wang, B., Qi, J., An, X., and Wang, Y. (2024). Heterogeneous Fusion of Biometric and Deep Physiological Features for Accurate Porcine Cough Recognition. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0297655"},{"key":"ref_110","first-page":"573","article-title":"A New Fusion Feature Based on Convolutional Neural Network for Pig Cough Recognition in Field Situations","volume":"8","author":"Shen","year":"2021","journal-title":"Inf. Process. Agric."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.biosystemseng.2022.05.010","article-title":"Investigation of Acoustic and Visual Features for Pig Cough Classification","volume":"219","author":"Ji","year":"2022","journal-title":"Biosyst. Eng."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Tsalera, E., Papadakis, A., and Samarakou, M. (2021). Comparison of Pre-Trained CNNs for Audio Classification Using Transfer Learning. J. Sens. Actuator Netw., 10.","DOI":"10.3390\/jsan10040072"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"104962","DOI":"10.1016\/j.psj.2025.104962","article-title":"Automated Detection of Broiler Vocalizations a Machine Learning Approach for Broiler Chicken Vocalization Monitoring","volume":"104","author":"Grzywalski","year":"2025","journal-title":"Poult. Sci."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"3447","DOI":"10.1007\/s13369-021-06297-w","article-title":"A Deep Learning Framework for Audio Deepfake Detection","volume":"47","author":"Khochare","year":"2022","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_115","first-page":"805","article-title":"A Study on Classification of Pig Sounds Based on Supervised Learning","volume":"70","author":"Min","year":"2021","journal-title":"J. Inst. Electr. Eng."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"101084","DOI":"10.1016\/j.ecoinf.2020.101084","article-title":"Data Augmentation Approaches for Improving Animal Audio Classification","volume":"57","author":"Nanni","year":"2020","journal-title":"Ecol. Inform."},{"key":"ref_117","unstructured":"Shankar, R., Nalla, N.R., and Muthulakshmi, M. (2023, January 14\u201317). Real-Time Pig Vocal Analysis for Early Respiratory Infection Study. Proceedings of the 2023 IEEE 20th India Council International Conference (INDICON), Hyderabad, India."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s13636-020-00175-3","article-title":"Ensemble of Convolutional Neural Networks to Improve Animal Audio Classification","volume":"2020","author":"Nanni","year":"2020","journal-title":"J. Audio Speech Music Proc."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/S0022-460X(03)00594-7","article-title":"Analysis of Pain-Related Vocalization in Young Pigs","volume":"266","author":"Marx","year":"2003","journal-title":"J. Sound Vib."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.5713\/ajas.2010.90483","article-title":"Classification of Porcine Wasting Diseases Using Sound Analysis","volume":"23","author":"Gutierrez","year":"2010","journal-title":"Asian-Australas. J. Anim. Sci."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.compag.2007.08.016","article-title":"Field Test of Algorithm for Automatic Cough Detection in Pig Houses","volume":"62","author":"Guarino","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"105203","DOI":"10.1016\/j.applanim.2020.105203","article-title":"Identification of Specific Call Types Produced by Pre-Weaning Gilts in Response to Isolation","volume":"234","author":"Olson","year":"2021","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"923","DOI":"10.3390\/ani3030923","article-title":"Understanding Vocalization Might Help to Assess Stressful Conditions in Piglets","volume":"3","author":"Oliveira","year":"2013","journal-title":"Animals"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Chae, H., Lee, J., Kim, J., Lee, S., Lee, J., Chung, Y., and Park, D. (2024). Novel Method for Detecting Coughing Pigs with Audio-Visual Multimodality for Smart Agriculture Monitoring. Sensors, 24.","DOI":"10.3390\/s24227232"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Ranmal, D., Ranasinghe, P., Paranayapa, T., Meedeniya, D., and Perera, C. (2024). ESC-NAS: Environment Sound Classification Using Hardware-Aware Neural Architecture Search for the Edge. Sensors, 24.","DOI":"10.3390\/s24123749"},{"key":"ref_126","unstructured":"Bibb\u00f3, G., Singh, A., and Plumbley, M.D. (2024). Environmental Sound Classification on an Embedded Hardware Platform. INTER-NOISE and NOISE-CON Congress and Conference Proceedings INTER-NOISE24, Nantes, France, Institute of Noise Control Engineering. Available online: https:\/\/ince.publisher.ingentaconnect.com\/content\/ince\/incecp\/2024\/00000270\/00000005\/art00041."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Mou, A., and Milanova, M. (2024). Performance Analysis of Deep Learning Model-Compression Techniques for Audio Classification on Edge Devices. Sci, 6.","DOI":"10.3390\/sci6020021"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"109025","DOI":"10.1016\/j.patcog.2022.109025","article-title":"Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices","volume":"133","author":"Mohaimenuzzaman","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"12133","DOI":"10.1007\/s00521-023-08345-y","article-title":"Neural Architecture Search for Energy-Efficient Always-on Audio Machine Learning","volume":"35","author":"Speckhard","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Srinivasagan, R., Sayed, M.S.E., Al-Rasheed, M.I., and Alzahrani, A.S. (2025). Edge Intelligence for Poultry Welfare: Utilizing Tiny Machine Learning Neural Network Processors for Vocalization Analysis. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0316920"},{"key":"ref_131","unstructured":"Rajesh, A., Ananthasubramanian, P., Raghavan, N., and Kumar, A. (2025). Memristive Nanowire Network for Energy Efficient Audio Classification: Pre-Processing-Free Reservoir Computing with Reduced Latency. arXiv."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1186\/s40537-024-00985-8","article-title":"Optimizing Poultry Audio Signal Classification with Deep Learning and Burn Layer Fusion","volume":"11","author":"Hassan","year":"2024","journal-title":"J. Big Data"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Upadhyaya, S., Buyens, W., Vranken, E., Desmet, W., and Karsmakers, P. (2023, January 15\u201317). Assessment of Data Augmentation and Transfer Learning for Making PIG Cough Classifier Robust to Changing Farm Conditions. Proceedings of the 2023 International Conference on Machine Learning and Applications (ICMLA), Jacksonville, FL, USA.","DOI":"10.1109\/ICMLA58977.2023.00141"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Chu, H.-C., Zhang, Y.-L., and Chiang, H.-C. (2023). A CNN Sound Classification Mechanism Using Data Augmentation. Sensors, 23.","DOI":"10.3390\/s23156972"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Jung, D.-H., Kim, N.Y., Moon, S.H., Jhin, C., Kim, H.-J., Yang, J.-S., Kim, H.S., Lee, T.S., Lee, J.Y., and Park, S.H. (2021). Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering. Animals, 11.","DOI":"10.3390\/ani11020357"}],"container-title":["Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-7737\/15\/2\/177\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:50:34Z","timestamp":1768812634000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-7737\/15\/2\/177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,18]]},"references-count":135,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["biology15020177"],"URL":"https:\/\/doi.org\/10.3390\/biology15020177","relation":{},"ISSN":["2079-7737"],"issn-type":[{"value":"2079-7737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,18]]}}}