{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T04:24:09Z","timestamp":1726806249781},"reference-count":53,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"RF Innovation","award":["CIFRE 2019\/1498"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Beehive health monitoring has gained interest in the study of bees in biology, ecology, and agriculture. As audio sensors are less intrusive, a number of audio datasets (mainly labeled with the presence of a queen in the hive) have appeared in the literature, and interest in their classification has been raised. All studies have exhibited good accuracy, and a few have questioned and revealed that classification cannot be generalized to unseen hives. To increase the number of known hives, a review of open datasets is described, and a merger in the form of the \u201cBeeTogether\u201d dataset on the open Kaggle platform is proposed. This common framework standardizes the data format and features while providing data augmentation techniques and a methodology for measuring hives\u2019 extrapolation properties. A classical classifier is proposed to benchmark the whole dataset, achieving the same good accuracy and poor hive generalization as those found in the literature. Insight into the role of the frequency of the classification of the presence of a queen is provided, and it is shown that this frequency mostly depends on a colony\u2019s belonging. New classifiers inspired by contrastive learning are introduced to circumvent the effect of colony belonging and obtain both good accuracy and hive extrapolation abilities when learning changes in labels. A process for obtaining absolute labels was prototyped on an unsupervised dataset. Solving hive extrapolation with a common open platform and contrastive approach can result in effective applications in agriculture.<\/jats:p>","DOI":"10.3390\/s24186067","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T15:51:46Z","timestamp":1726761106000},"page":"6067","source":"Crossref","is-referenced-by-count":0,"title":["Bee Together: Joining Bee Audio Datasets for Hive Extrapolation in AI-Based Monitoring"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"http:\/\/orcid.org\/0009-0006-2953-2272","authenticated-orcid":false,"given":"Augustin","family":"Bricout","sequence":"first","affiliation":[{"name":"Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), University of Toulouse, 31077 Toulouse, France"},{"name":"RF Innovation, 20 Avenue Didier Daurat, 31400 Toulouse, France"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3760-4698","authenticated-orcid":false,"given":"Philippe","family":"Leleux","sequence":"additional","affiliation":[{"name":"Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), University of Toulouse, 31077 Toulouse, France"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6563-0842","authenticated-orcid":false,"given":"Pascal","family":"Acco","sequence":"additional","affiliation":[{"name":"Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), University of Toulouse, 31077 Toulouse, France"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8054-714X","authenticated-orcid":false,"given":"Christophe","family":"Escriba","sequence":"additional","affiliation":[{"name":"Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), University of Toulouse, 31077 Toulouse, France"}]},{"given":"Jean-Yves","family":"Fourniols","sequence":"additional","affiliation":[{"name":"Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), University of Toulouse, 31077 Toulouse, France"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3823-1635","authenticated-orcid":false,"given":"Georges","family":"Soto-Romero","sequence":"additional","affiliation":[{"name":"Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), University of Toulouse, 31077 Toulouse, France"}]},{"given":"R\u00e9mi","family":"Floquet","sequence":"additional","affiliation":[{"name":"RF Innovation, 20 Avenue Didier Daurat, 31400 Toulouse, France"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Abdollahi, M., Giovenazzo, P., and Falk, T.H. (2022). Automated Beehive Acoustics Monitoring: A Comprehensive Review of the Literature and Recommendations for Future Work. Appl. Sci., 12.","DOI":"10.3390\/app12083920"},{"key":"ref_2","first-page":"100726","article-title":"Recent developments on precision beekeeping: A systematic literature review","volume":"14","author":"Alleri","year":"2023","journal-title":"J. Agric. Food Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107906","DOI":"10.1016\/j.compag.2023.107906","article-title":"A Framework for Better Sensor-Based Beehive Health Monitoring","volume":"210","author":"Zaman","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/scientificamerican0464-116","article-title":"Sound Communication in Honeybees","volume":"210","author":"Wenner","year":"1964","journal-title":"Sci. Am."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/BF00603817","article-title":"The tooting and quacking vibration signals of honeybee queens: A quantitative analysis","volume":"158","author":"Michelsen","year":"1986","journal-title":"J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1051\/apido:19930309","article-title":"Acoustical communication in honeybees","volume":"24","author":"Kirchner","year":"1993","journal-title":"Apidologie"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/BF00290824","article-title":"Sound and vibrational signals in the dance language of the honeybee, Apis mellifera","volume":"18","author":"Michelsen","year":"1986","journal-title":"Behav. Ecol. Sociobiol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rigakis, I., Potamitis, I., Tatlas, N.A., Psirofonia, G., Tzagaraki, E., and Alissandrakis, E. (2023). A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring. Sensors, 23.","DOI":"10.3390\/s23031407"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.compag.2011.01.004","article-title":"Identification of the honey bee swarming process by analysing the time course of hive vibrations","volume":"76","author":"Bencsik","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.compag.2008.05.010","article-title":"Monitoring of swarming sounds in bee hives for early detection of the swarming period","volume":"64","author":"Ferrari","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","first-page":"297","article-title":"Detection of the bee queen presence using sound analysis","volume":"Volume 10752","author":"Cejrowski","year":"2018","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_12","unstructured":"Nolasco, I., and Benetos, E. (2018). To bee or not to bee: Investigating machine learning approaches for beehive sound recognition. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Terenzi, A., Cecchi, S., and Spinsante, S. (2020). On the importance of the sound emitted by honey bee hives. Vet. Sci., 7.","DOI":"10.3390\/vetsci7040168"},{"key":"ref_14","first-page":"200115","article-title":"Environmental Sound Classification: A descriptive review of the literature","volume":"16","author":"Bansal","year":"2022","journal-title":"Intell. Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106604","DOI":"10.1016\/j.compag.2021.106604","article-title":"Toward an intelligent and efficient beehive: A survey of precision beekeeping systems and services","volume":"192","author":"Hadjur","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.compag.2019.02.024","article-title":"Analysis of a multiclass classification problem by Lasso Logistic Regression and Singular Value Decomposition to identify sound patterns in queenless bee colonies","volume":"159","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Orlowska, A., Fourer, D., Gavini, J.P., and Cassou-Ribehart, D. (2021, January 13\u201315). Honey Bee Queen Presence Detection from Audio Field Recordings using Summarized Spectrogram and Convolutional Neural Networks. Proceedings of the 21st International Conference on Intelligent Systems Design and Applications (ISDA 2021), Seattle, WA, USA.","DOI":"10.1007\/978-3-030-96308-8_8"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"117104","DOI":"10.1016\/j.eswa.2022.117104","article-title":"MFCC-based descriptor for bee queen presence detection","volume":"201","author":"Soares","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kampelopoulos, D., Sofianidis, I., Tananaki, C., Tsiapali, K., Nikolaidis, S., and Siozios, K. (2022). Analyzing the Beehive\u2019s Sound to Monitor the Presence of the Queen Bee, Institute of Electrical and Electronics Engineers Inc.","DOI":"10.1109\/PACET56979.2022.9976374"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Barbisan, L., Turvani, G., and Fabrizio, R. (2023). Audio-Based Identification of Queen Bee Presence Inside Beehives, Institute of Electrical and Electronics Engineers Inc.","DOI":"10.1109\/CAFE58535.2023.10291679"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kanelis, D., Liolios, V., Papadopoulou, F., Rodopoulou, M.A., Kampelopoulos, D., Siozios, K., and Tananaki, C. (2023). Decoding the Behavior of a Queenless Colony Using Sound Signals. Biology, 12.","DOI":"10.3390\/biology12111392"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nolasco, I., Terenzi, A., Cecchi, S., Orcioni, S., Bear, H.L., and Benetos, E. (2019, January 12\u201317). Audio-based Identification of Beehive States. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682981"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/TASLP.2021.3133194","article-title":"Comparison of Feature Extraction Methods for Sound-Based Classification of Honey Bee Activity","volume":"30","author":"Terenzi","year":"2022","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107589","DOI":"10.1016\/j.compag.2022.107589","article-title":"Acoustic and vibration monitoring of honeybee colonies for beekeeping-relevant aspects of presence of queen bee and swarming","volume":"205","author":"Uthoff","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","unstructured":"Nolasco, I., Cecchi, A.T.S., Orcioni, S., and Benetos, H.L.B.E. (2024, September 13). Audio-Based Identification of Beehive states: The Dataset. NuHive Zenodo Dataset. Available online: https:\/\/zenodo.org\/records\/2667806."},{"key":"ref_26","unstructured":"(2024, September 13). TBON Kaggle Dataset. To Bee or Not to Bee. Available online: https:\/\/www.kaggle.com\/datasets\/chrisfilo\/to-bee-or-no-to-bee."},{"key":"ref_27","unstructured":"Calvo, J.A. (2024, September 13). Open Source Behive Project. OSBH Zenodo Dataset. Available online: https:\/\/zenodo.org\/records\/321345."},{"key":"ref_28","unstructured":"Bricout, A. (2024, September 13). Bee Together. BT Kaggle Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/augustin23\/beetogether\/data."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5594498","DOI":"10.1155\/2021\/5594498","article-title":"Acoustic Scene Classification and Visualization of Beehive Sounds Using Machine Learning Algorithms and Grad-CAM","volume":"2021","author":"Kim","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kulyukin, V. (2021). Audio, image, video, and weather datasets for continuous electronic beehive monitoring. Appl. Sci., 11.","DOI":"10.3390\/app11104632"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ruvinga, S., Hunter, G., Duran, O., and Nebel, J.C. (2023). Identifying Queenlessness in Honeybee Hives from Audio Signals Using Machine Learning. Electronics, 12.","DOI":"10.3390\/electronics12071627"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kulyukin, V., Mukherjee, S., and Amlathe, P. (2018). Toward audio beehive monitoring: Deep learning vs. standard machine learning in classifying beehive audio samples. Appl. Sci., 8.","DOI":"10.3390\/app8091573"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zgank, A. (2020). Bee swarm activity acoustic classification for an iot-based farm service. Sensors, 20.","DOI":"10.3390\/s20010021"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zgank, A. (2021). Iot-based bee swarm activity acoustic classification using deep neural networks. Sensors, 21.","DOI":"10.3390\/s21030676"},{"key":"ref_35","first-page":"13","article-title":"Identify the Beehive Sound using Deep Learning","volume":"14","author":"Quaderi","year":"2022","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Robles-Guerrero, A., Saucedo-Anaya, T., Guerrero-Mendez, C.A., G\u00f3mez-Jim\u00e9nez, S., and Navarro-Sol\u00eds, D.J. (2023). Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational Resources. Sensors, 23.","DOI":"10.3390\/s23010460"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e14696","DOI":"10.7717\/peerj.14696","article-title":"Applicability of VGGish embedding in bee colony monitoring: Comparison with MFCC in colony sound classification","volume":"11","author":"Di","year":"2023","journal-title":"PeerJ"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"102274","DOI":"10.1016\/j.ecoinf.2023.102274","article-title":"A deep learning-based approach for bee sound identification","volume":"78","author":"Truong","year":"2023","journal-title":"Ecol. Inform."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Farina, A. (2023). Discovering ecoacoustic codes in beehives: First evidence and perspectives. BioSystems, 234.","DOI":"10.1016\/j.biosystems.2023.105041"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5873","DOI":"10.1007\/s00500-022-07596-6","article-title":"Investigation on new Mel frequency cepstral coefficients features and hyper-parameters tuning technique for bee sound recognition","volume":"27","author":"Phan","year":"2023","journal-title":"Soft Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"23269","DOI":"10.1007\/s11042-023-15192-5","article-title":"Bee detection in bee hives using selective features from acoustic data","volume":"83","author":"Rustam","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_42","unstructured":"(2024, September 13). TBON Processed Kaggle Dataset. Beehive Buzz Anomalies. Available online: https:\/\/www.kaggle.com\/datasets\/yevheniiklymenko\/beehive-buzz-anomalies."},{"key":"ref_43","unstructured":"Yang, A. (2024, September 13). Smart Bee Colony Monitor: Clips of Beehive Sounds. SBCM Kaggle Dataset. Available online: https:\/\/www.kaggle.com\/dsv\/4451415."},{"key":"ref_44","unstructured":"and Varkonyi, D.T. (2024, September 13). Beehive Audio Recordings. BAD Zenodo Dataset. Available online: https:\/\/zenodo.org\/records\/7052981."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"106489","DOI":"10.1016\/j.compag.2021.106489","article-title":"Buzz-based honeybee colony fingerprint","volume":"191","author":"Cejrowski","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_46","unstructured":"McFee, B., McVicar, M., Faronbi, D., Roman, I., Gover, M., Balke, S., Seyfarth, S., Malek, A., Raffel, C., and Lostanlen, V. (2024, September 13). librosa\/librosa: 0.10.2.post1. Available online: https:\/\/zenodo.org\/records\/11192913."},{"key":"ref_47","unstructured":"Champion, P. (2023). Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques. [Ph.D. Thesis, Universit\u00e9 de Lorraine]."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kim, G., Kim, G., Han, D.K., Han, D.K., Han, D.K., Ko, H., and Ko, H. (2021). SpecMix: A Mixed Sample Data Augmentation Method for Training with Time-Frequency Domain Features. arXiv.","DOI":"10.31219\/osf.io\/ubcft"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hu, T.Y., Hu, T.Y., Hu, T.Y., Hu, T.Y., Shrivastava, A., Shrivastava, A., Chang, J.H.R., Chang, R., Chang, J.H.R., and Chang, J.H.R. (2021, January 6\u201311). SapAugment: Learning A Sample Adaptive Policy for Data Augmentation. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9413928"},{"key":"ref_50","unstructured":"Bromley, J., Guyon, I., LeCun, Y., S\u00e4ckinger, E., and Shah, R. (December, January 29). Signature verification using a \u201csiamese\u201d time delay neural network. Proceedings of the Advances in Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Umesh, S., Cohen, L., and Nelson, D. (1999, January 15\u201319). Fitting the Mel scale. Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP\u201999) (Cat. No.99CH36258), Phoenix, AZ, USA.","DOI":"10.1109\/ICASSP.1999.758101"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Mahoney, J., and Schensul, D. (2006). 454 Historical Context and Path Dependence. The Oxford Handbook of Contextual Political Analysis, Oxford University Press.","DOI":"10.1093\/oxfordhb\/9780199270439.003.0024"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1088\/0508-3443\/7\/5\/302","article-title":"A re-determination of the equal-loudness relations for pure tones","volume":"7","author":"Robinson","year":"1956","journal-title":"Br. J. Appl. Phys."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6067\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T15:55:50Z","timestamp":1726761350000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6067"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,19]]},"references-count":53,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24186067"],"URL":"https:\/\/doi.org\/10.3390\/s24186067","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,19]]}}}