{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:26:33Z","timestamp":1778693193757,"version":"3.51.4"},"reference-count":160,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T00:00:00Z","timestamp":1667174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Automated bioacoustics classification has received increasing attention from the research community in recent years due its cross-disciplinary nature and its diverse application. Applications in bioacoustics classification range from smart acoustic sensor networks that investigate the effects of acoustic vocalizations on species to context-aware edge devices that anticipate changes in their environment adapt their sensing and processing accordingly. The research described here is an in-depth survey of the current state of bioacoustics classification and monitoring. The survey examines bioacoustics classification alongside general acoustics to provide a representative picture of the research landscape. The survey reviewed 124 studies spanning eight years of research. The survey identifies the key application areas in bioacoustics research and the techniques used in audio transformation and feature extraction. The survey also examines the classification algorithms used in bioacoustics systems. Lastly, the survey examines current challenges, possible opportunities, and future directions in bioacoustics.<\/jats:p>","DOI":"10.3390\/s22218361","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T06:49:02Z","timestamp":1667371742000},"page":"8361","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Review of Automated Bioacoustics and General Acoustics Classification Research"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5164-6424","authenticated-orcid":false,"given":"Leah","family":"Mutanu","sequence":"first","affiliation":[{"name":"Department of Computing, United States International University Africa, Nairobi P.O. Box 14634-0800, Kenya"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1721-8837","authenticated-orcid":false,"given":"Jeet","family":"Gohil","sequence":"additional","affiliation":[{"name":"Department of Computing, United States International University Africa, Nairobi P.O. Box 14634-0800, Kenya"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khushi","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sam Houston State University, Huntsville, TX 77341, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Perpetua","family":"Wagio","sequence":"additional","affiliation":[{"name":"Department of Computing, United States International University Africa, Nairobi P.O. 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(2019, January 9\u201312). Iot based urban noise monitoring in deep learning using historical reports. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9006176"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Vacher, M., Serignat, J.F., Chaillol, S., Istrate, D., and Popescu, V. (2006). Speech and sound use in a remote monitoring system for health care. International Conference on Text, Speech and Dialogue, Springer.","DOI":"10.1007\/11846406_89"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MIM.2021.9549233","article-title":"Audio information retrieval and musical acoustics","volume":"24","author":"Olivieri","year":"2021","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.pbi.2016.06.011","article-title":"Acoustic communication in plant\u2013animal interactions","volume":"32","author":"Simon","year":"2016","journal-title":"Curr. Opin. Plant Biol."},{"key":"ref_7","first-page":"68","article-title":"Bioacoustics approaches in biodiversity inventories","volume":"8","author":"Obrist","year":"2010","journal-title":"Abc Taxa"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14015991","DOI":"10.1017\/ATSIP.2014.12","article-title":"Environmental sound recognition: A survey","volume":"3","author":"Chachada","year":"2014","journal-title":"APSIPA Trans. Signal Inf. Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"57684","DOI":"10.1109\/ACCESS.2020.2978547","article-title":"Bioacoustics data analysis\u2014A taxonomy, survey and open challenges","volume":"8","author":"Kvsn","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"20190225","DOI":"10.1098\/rsif.2019.0225","article-title":"Automated bioacoustics: Methods in ecology and conservation and their potential for animal welfare monitoring","volume":"16","author":"Mcloughlin","year":"2019","journal-title":"J. R. Soc. Interface"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Walters, C.L., Collen, A., Lucas, T., Mroz, K., Sayer, C.A., and Jones, K.E. (2013). Challenges of using bioacoustics to globally monitor bats. Bat Evolution, Ecology, and Conservation, Springer.","DOI":"10.1007\/978-1-4614-7397-8_23"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3575","DOI":"10.1007\/s10462-020-09932-4","article-title":"Bioacoustic signal denoising: A review","volume":"54","author":"Xie","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, W., Sun, Q., Chen, X., Xie, G., Wu, H., and Xu, C. (2021). Deep learning methods for heart sounds classification: A systematic review. Entropy, 23.","DOI":"10.3390\/e23060667"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1681","DOI":"10.1603\/029.102.0436","article-title":"On automatic bioacoustic detection of pests: The cases of Rhynchophorus ferrugineus and Sitophilus oryzae","volume":"102","author":"Potamitis","year":"2009","journal-title":"J. Econ. Entomol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e488","DOI":"10.7717\/peerj.488","article-title":"Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning","volume":"2","author":"Stowell","year":"2014","journal-title":"PeerJ"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bonet-Sol\u00e0, D., and Alsina-Pag\u00e8s, R.M. (2021). A comparative survey of feature extraction and machine learning methods in diverse acoustic environments. Sensors, 21.","DOI":"10.3390\/s21041274"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lima, M.C.F., de Almeida Leandro, M.E.D., Valero, C., Coronel, L.C.P., and Bazzo, C.O.G. (2020). Automatic detection and monitoring of insect pests\u2014A review. Agriculture, 10.","DOI":"10.3390\/agriculture10050161"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Stowell, D., Wood, M., Stylianou, Y., and Glotin, H. (2016, January 13\u201316). Bird detection in audio: A survey and a challenge. Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Vietri sul Mare, Italy.","DOI":"10.1109\/MLSP.2016.7738875"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1109\/JBHI.2020.3012666","article-title":"Can machine learning assist locating the excitation of snore sound? A review","volume":"25","author":"Qian","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bhattacharya, S., Das, N., Sahu, S., Mondal, A., and Borah, S. (2021). Deep classification of sound: A concise review. Proceeding of First Doctoral Symposium on Natural Computing Research, Springer.","DOI":"10.1007\/978-981-33-4073-2_4"},{"key":"ref_21","first-page":"389","article-title":"Detection of acoustic signals from Distributed Acoustic Sensor data with Random Matrix Theory and their classification using Machine Learning","volume":"Volume 11525","author":"Bencharif","year":"2020","journal-title":"SPIE Future Sensing Technologies"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","unstructured":"Piczak, K.J. (2015, January 13). ESC: Dataset for environmental sound classification. Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia.","DOI":"10.1145\/2733373.2806390"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gharib, S., Derrar, H., Niizumi, D., Senttula, T., Tommola, J., Heittola, T., Virtanen, T., and Huttunen, H. (2018, January 17\u201320). Acoustic scene classification: A competition review. Proceedings of the 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), Aalborg, Denmark.","DOI":"10.1109\/MLSP.2018.8517000"},{"key":"ref_25","first-page":"134","article-title":"A knowledge development perspective on literature reviews: Validation of a new typology in the IS field","volume":"46","author":"Schryen","year":"2020","journal-title":"Commun. AIS"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1080\/0960085X.2017.1398880","article-title":"Transparency in literature reviews: An assessment of reporting practices across review types and genres in top IS journals","volume":"27","author":"Templier","year":"2018","journal-title":"Eur. J. Inf. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/2046-4053-4-1","article-title":"Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement","volume":"4","author":"Moher","year":"2015","journal-title":"Syst. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e2117485119","DOI":"10.1073\/pnas.2117485119","article-title":"Anti-bat ultrasound production in moths is globally and phylogenetically widespread","volume":"119","author":"Barber","year":"2022","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_29","unstructured":"Bahuleyan, H. (2018). Music genre classification using machine learning techniques. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1109\/TCYB.2019.2941281","article-title":"Identity recognition based on bioacoustics of human body","volume":"51","author":"Sim","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6532","DOI":"10.1038\/s41598-018-24926-7","article-title":"Comparing SVM and ANN based machine learning methods for species identification of food contaminating beetles","volume":"8","author":"Bisgin","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e2002545117","DOI":"10.1073\/pnas.2002545117","article-title":"Deep learning and computer vision will transform entomology","volume":"118","author":"Bjerge","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1007\/s42452-020-2568-8","article-title":"Deep neural network with moth search optimization algorithm based detection and classification of diabetic retinopathy images","volume":"2","author":"Shankar","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bjerge, K., Nielsen, J.B., Sepstrup, M.V., Helsing-Nielsen, F., and H\u00f8ye, T.T. (2021). An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning. Sensors, 21.","DOI":"10.3390\/s21020343"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.anbehav.2016.12.005","article-title":"Applications of machine learning in animal behaviour studies","volume":"124","author":"Valletta","year":"2017","journal-title":"Anim. Behav."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.patcog.2015.09.012","article-title":"A software system for automated identification and retrieval of moth images based on wing attributes","volume":"51","author":"Feng","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1038\/s41597-020-00725-6","article-title":"An annotated dataset of bioacoustic sensing and features of mosquitoes","volume":"7","author":"Vasconcelos","year":"2020","journal-title":"Sci. Data"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.knosys.2006.11.012","article-title":"Automatic species identification of live moths","volume":"20","author":"Mayo","year":"2007","journal-title":"Knowl.-Based Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/09524622.2012.669664","article-title":"A comparative study in birds: Call-type-independent species and individual recognition using four machine-learning methods and two acoustic features","volume":"21","author":"Cheng","year":"2012","journal-title":"Bioacoustics"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1007\/s10994-018-5739-8","article-title":"A comparison of hierarchical multi-output recognition approaches for anuran classification","volume":"107","author":"Colonna","year":"2018","journal-title":"Mach. Learn."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"102115","DOI":"10.1016\/j.adhoc.2020.102115","article-title":"A multi-view CNN-based acoustic classification system for automatic animal species identification","volume":"102","author":"Xu","year":"2020","journal-title":"Ad. Hoc Netw."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.future.2021.06.019","article-title":"A novel frog chorusing recognition method with acoustic indices and machine learning","volume":"125","author":"Gan","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.apacoust.2016.06.029","article-title":"Acoustic classification of Australian frogs based on enhanced features and machine learning algorithms","volume":"113","author":"Xie","year":"2016","journal-title":"Appl. Acoust."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1038\/s41598-021-81005-0","article-title":"Advances in automatic identification of flying insects using optical sensors and machine learning","volume":"11","author":"Kirkeby","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tacioli, L., Toledo, L., and Medeiros, C. (2017). An architecture for animal sound identification based on multiple feature extraction and classification algorithms. Anais do XI Brazilian e-Science Workshop, Sociedade Brasileira de Computa\u00e7\u00e3o.","DOI":"10.5753\/bresci.2017.9919"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"10997","DOI":"10.1038\/s41598-019-47335-w","article-title":"ORCA-SPOT: An automatic killer whale sound detection toolkit using deep learning","volume":"9","author":"Bergler","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, L., Saleh, I., Thapaliya, S., Louie, J., Figueroa-Hernandez, J., and Ji, H. (2017, January 14\u201316). An empirical evaluation of machine learning approaches for species identification through bioacoustics. Proceedings of the 2017 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.","DOI":"10.1109\/CSCI.2017.82"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"\u015ea\u015fmaz, E., and Tek, F.B. (2018, January 20\u201323). Animal sound classification using a convolutional neural network. Proceedings of the 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, Bosnia and Herzegovina.","DOI":"10.1109\/UBMK.2018.8566449"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Nanni, L., Brahnam, S., Lumini, A., and Maguolo, G. (2020). Animal sound classification using dissimilarity spaces. Appl. Sci., 10.","DOI":"10.20944\/preprints202010.0526.v1"},{"key":"ref_51","unstructured":"Romero, J., Luque, A., and Carrasco, A. (2011, January 21\u201323). Animal Sound Classification using Sequential Classifiers. Proceedings of the BIOSIGNALS, Porto, Portugal."},{"key":"ref_52","first-page":"3384","article-title":"Animal sounds classification scheme based on multi-feature network with mixed datasets","volume":"14","author":"Kim","year":"2020","journal-title":"KSII Trans. Internet Inf. Syst. (TIIS)"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Weninger, F., and Schuller, B. (2011, January 22\u201327). Audio recognition in the wild: Static and dynamic classification on a real-world database of animal vocalizations. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5946409"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1079\/BER2004306","article-title":"Automated identification of field-recorded songs of four British grasshoppers using bioacoustic signal recognition","volume":"94","author":"Chesmore","year":"2004","journal-title":"Bull. Entomol. Res."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Mac Aodha, O., Gibb, R., Barlow, K.E., Browning, E., Firman, M., Freeman, R., Harder, B., Kinsey, L., Mead, G.R., and Newson, S.E. (2018). Bat detective\u2014Deep learning tools for bat acoustic signal detection. PLoS Comput. Biol., 14.","DOI":"10.1371\/journal.pcbi.1005995"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zgank, A. (2019). Bee swarm activity acoustic classification for an IoT-based farm service. Sensors, 20.","DOI":"10.3390\/s20010021"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1121\/10.0000921","article-title":"Beluga whale acoustic signal classification using deep learning neural network models","volume":"147","author":"Zhong","year":"2020","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"15733","DOI":"10.1038\/s41598-021-95076-6","article-title":"Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture","volume":"11","author":"Hossain","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1007\/s00521-018-3626-7","article-title":"Bioacoustic detection with wavelet-conditioned convolutional neural networks","volume":"32","author":"Kiskin","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_60","unstructured":"Pourhomayoun, M., Dugan, P., Popescu, M., and Clark, C. (2013). Bioacoustic signal classification based on continuous region processing, grid masking and artificial neural network. arXiv."},{"key":"ref_61","first-page":"68530","article-title":"Birds sound classification based on machine learning algorithms","volume":"9","author":"Mehyadin","year":"2021","journal-title":"Asian J. Res. Comput. Sci."},{"key":"ref_62","unstructured":"Arzar, N.N.K., Sabri, N., Johari, N.F.M., Shari, A.A., Noordin, M.R.M., and Ibrahim, S. (2019, January 29). Butterfly species identification using convolutional neural network (CNN). Proceedings of the 2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), Selangor, Malaysia."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1121\/1.4861348","article-title":"Classification of large acoustic datasets using machine learning and crowdsourcing: Application to whale calls","volume":"135","author":"Shamir","year":"2014","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s10071-007-0129-9","article-title":"Classification of dog barks: A machine learning approach","volume":"11","author":"Kaplan","year":"2008","journal-title":"Anim. Cogn."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Gunasekaran, S., and Revathy, K. (2010, January 9\u201311). Content-based classification and retrieval of wild animal sounds using feature selection algorithm. Proceedings of the 2010 Second International Conference on Machine Learning and Computing, Bangalore, India.","DOI":"10.1109\/ICMLC.2010.11"},{"key":"ref_66","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_67","doi-asserted-by":"crossref","unstructured":"Ko, K., Park, S., and Ko, H. (2018, January 18\u201321). Convolutional feature vectors and support vector machine for animal sound classification. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512408"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"12588","DOI":"10.1038\/s41598-019-48909-4","article-title":"Deep machine learning techniques for the detection and classification of sperm whale bioacoustics","volume":"9","author":"Bermant","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1121\/1.5118245","article-title":"Deep metric learning for bioacoustic classification: Overcoming training data scarcity using dynamic triplet loss","volume":"146","author":"Thakur","year":"2019","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1121\/10.0005475","article-title":"Deep perceptual embeddings for unlabelled animal sound events","volume":"150","author":"Morfi","year":"2021","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1016\/j.patrec.2009.09.014","article-title":"Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring","volume":"31","author":"Bardeli","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2808","DOI":"10.1093\/jee\/tov231","article-title":"Detection of adult beetles inside the stored wheat mass based on their acoustic emissions","volume":"108","author":"Eliopoulos","year":"2015","journal-title":"J. Econ. Entomol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s12938-018-0448-x","article-title":"Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques","volume":"17","author":"Kim","year":"2018","journal-title":"Biomed. Eng. Online"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Yazga\u00e7, B.G., K\u0131rc\u0131, M., and K\u0131van, M. (2016, January 18\u201320). Detection of sunn pests using sound signal processing methods. Proceedings of the 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Tianjin, China.","DOI":"10.1109\/Agro-Geoinformatics.2016.7577694"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Pandeya, Y.R., Kim, D., and Lee, J. (2018). Domestic cat sound classification using learned features from deep neural nets. Appl. Sci., 8.","DOI":"10.3390\/app8101949"},{"key":"ref_76","first-page":"31","article-title":"Energy efficient animal sound recognition scheme in wireless acoustic sensors networks","volume":"12","year":"2020","journal-title":"Int. J. Wirel. Mob. Netw. (IJWMN)"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"3737","DOI":"10.1016\/j.eswa.2008.02.059","article-title":"Frog classification using machine learning techniques","volume":"36","author":"Huang","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Salamon, J., Bello, J.P., Farnsworth, A., and Kelling, S. (2017, January 5\u20139). Fusing shallow and deep learning for bioacoustic bird species classification. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952134"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.ecoinf.2019.05.007","article-title":"Handcrafted features and late fusion with deep learning for bird sound classification","volume":"52","author":"Xie","year":"2019","journal-title":"Ecol. Inform."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Chao, K.W., Hu, N.Z., Chao, Y.C., Su, C.K., and Chiu, W.H. (2019). Implementation of artificial intelligence for classification of frogs in bioacoustics. Symmetry, 11.","DOI":"10.3390\/sym11121454"},{"key":"ref_81","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_82","doi-asserted-by":"crossref","unstructured":"Ribeiro, A.P., da Silva, N.F.F., Mesquita, F.N., Ara\u00fajo, P.d.C.S., Rosa, T.C., and Mesquita-Neto, J.N. (2021). Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds. PLoS Comput. Biol., 17.","DOI":"10.1371\/journal.pcbi.1009426"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.eswa.2015.12.020","article-title":"Methodology for automatic bioacoustic classification of anurans based on feature fusion","volume":"50","author":"Noda","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Chalmers, C., Fergus, P., Wich, S., and Longmore, S. (2021, January 18\u201322). Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China.","DOI":"10.1109\/IJCNN52387.2021.9534195"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"267","DOI":"10.3389\/fmars.2020.00267","article-title":"Monitoring of a nearshore small dolphin species using passive acoustic platforms and supervised machine learning techniques","volume":"7","author":"Caruso","year":"2020","journal-title":"Front. Mar. Sci."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"107375","DOI":"10.1016\/j.apacoust.2020.107375","article-title":"Multispecies bioacoustic classification using transfer learning of deep convolutional neural networks with pseudo-labeling","volume":"166","author":"Zhong","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Kim, D., Lee, Y., and Ko, H. Multi-Task Learning for Animal Species and Group Category Classification. Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City, Guangzhou, China.","DOI":"10.1145\/3377170.3377259"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Dugan, P.J., Rice, A.N., Urazghildiiev, I.R., and Clark, C.W. (2010, January 7). North Atlantic right whale acoustic signal processing: Part I. Comparison of machine learning recognition algorithms. In Proceedings of the 2010 IEEE Long Island Systems, Applications and Technology Conference, Farmingdale, NY, USA.","DOI":"10.1109\/LISAT.2010.5478268"},{"key":"ref_89","unstructured":"Balemarthy, S., Sajjanhar, A., and Zheng, J.X. (2018). Our practice of using machine learning to recognize species by voice. arXiv."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/09524622.2016.1190946","article-title":"Predicting species identity of bumblebees through analysis of flight buzzing sounds","volume":"26","author":"Gams","year":"2017","journal-title":"Bioacoustics"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Lostanlen, V., Salamon, J., Farnsworth, A., Kelling, S., and Bello, J.P. (2019). Robust sound event detection in bioacoustic sensor networks. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0214168"},{"key":"ref_92","first-page":"141","article-title":"Using machine learning techniques to classify cricket sound","volume":"Volume 11384","author":"Xie","year":"2019","journal-title":"Eleventh International Conference on Signal Processing Systems"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Nanni, L., Rigo, A., Lumini, A., and Brahnam, S. (2020). Spectrogram classification using dissimilarity space. Appl. Sci., 10.","DOI":"10.3390\/app10124176"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Salamon, J., Bello, J.P., Farnsworth, A., Robbins, M., Keen, S., Klinck, H., and Kelling, S. (2016). Towards the automatic classification of avian flight calls for bioacoustic monitoring. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0166866"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s13592-018-0619-6","article-title":"Automated classification of bees and hornet using acoustic analysis of their flight sounds","volume":"50","author":"Kawakita","year":"2019","journal-title":"Apidologie"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"3861","DOI":"10.1121\/10.0007291","article-title":"Automated classification of Tursiops aduncus whistles based on a depth-wise separable convolutional neural network and data augmentation","volume":"150","author":"Li","year":"2021","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"EL541","DOI":"10.1121\/1.5111975","article-title":"Automatic acoustic classification of insect species based on directed acyclic graphs","volume":"145","author":"Ntalampiras","year":"2019","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1007\/s10905-014-9454-4","article-title":"Flying insect classification with inexpensive sensors","volume":"27","author":"Chen","year":"2014","journal-title":"J. Insect Behav."},{"key":"ref_99","unstructured":"Zhu, L.-Q. (2011, January 14\u201315). Insect sound recognition based on mfcc and pnn. Proceedings of the 2011 International Conference on Multimedia and Signal Processing, Guilin, China."},{"key":"ref_100","first-page":"49","article-title":"Insect Inspection on the basis of their Flight Sound","volume":"6","author":"Hussain","year":"2015","journal-title":"Int. J. Sci. Eng. Res."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"e103","DOI":"10.7717\/peerj.103","article-title":"Real-time bioacoustics monitoring and automated species identification","volume":"1","author":"Aide","year":"2013","journal-title":"PeerJ"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Rathore, D.S., Ram, B., Pal, B., and Malviya, S. (2019, January 8\u20139). Analysis of classification algorithms for insect detection using MATLAB. Proceedings of the 2nd International Conference on Advanced Computing and Software Engineering (ICACSE), Sultanpur, India.","DOI":"10.2139\/ssrn.3350283"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.1111\/ele.13092","article-title":"Animal Sound Identifier (ASI): Software for automated identification of vocal animals","volume":"21","author":"Ovaskainen","year":"2018","journal-title":"Ecol. Lett."},{"key":"ref_104","unstructured":"M\u00fcller, L., and Marti, M. (2018, January 10\u201314). Bird Sound Classification using a Bidirectional LSTM. Proceedings of the CLEF (Working Notes), Avignon, France."},{"key":"ref_105","first-page":"4708","article-title":"Classification of birds based on their sound patterns using GMM and SVM classifiers","volume":"5","author":"Supriya","year":"2018","journal-title":"Int. Res. J. Eng. Technol."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1121\/1.5087827","article-title":"Deep convolutional network for animal sound classification and source attribution using dual audio recordings","volume":"145","author":"Oikarinen","year":"2019","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.aci.2018.06.002","article-title":"Ensemble of convolutional neural networks for bioimage classification","volume":"17","author":"Nanni","year":"2020","journal-title":"Appl. Comput. Inform."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2043612.2043613","article-title":"Video accessibility enhancement for hearing-impaired users","volume":"7","author":"Hong","year":"2011","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl. (TOMM)"},{"key":"ref_109","unstructured":"Wang, W., Chen, Z., Xing, B., Huang, X., Han, S., and Agu, E. A smartphone-based digital hearing aid to mitigate hearing loss at specific frequencies. Proceedings of the 1st Workshop on Mobile Medical Applications, Seattle, WA, USA."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Bountourakis, V., Vrysis, L., and Papanikolaou, G. Machine learning algorithms for environmental sound recognition: Towards soundscape semantics. Proceedings of the Audio Mostly 2015 on Interaction with Sound, Thessaloniki Greece.","DOI":"10.1145\/2814895.2814905"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Li, M., Gao, Z., Zang, X., and Wang, X. (2018, January 19\u201321). Environmental noise classification using convolution neural networks. Proceedings of the 2018 International Conference on Electronics and Electrical Engineering Technology, Tianjin, China.","DOI":"10.1145\/3277453.3277481"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Alsouda, Y., Pllana, S., and Kurti, A. (2019, January 5\u20137). Iot-based urban noise identification using machine learning: Performance of SVM, KNN, bagging, and random forest. Proceedings of the International Conference on Omni-Layer Intelligent Systems, Crete, Greece.","DOI":"10.1145\/3312614.3312631"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Kurnaz, S., and Aljabery, M.A. (2018, January 19\u201320). Predict the type of hearing aid of audiology patients using data mining techniques. Proceedings of the Fourth International Conference on Engineering & MIS 2018, Istanbul, Turkey.","DOI":"10.1145\/3234698.3234755"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Wang, W., Seraj, F., Meratnia, N., and Havinga, P.J. (2019, January 5\u20137). Privacy-aware environmental sound classification for indoor human activity recognition. Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Island of Rhodes, Greece.","DOI":"10.1145\/3316782.3321521"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Seker, H., and Inik, O. (2020, January 9\u201311). CnnSound: Convolutional Neural Networks for the Classification of Environmental Sounds. Proceedings of the 2020 4th International Conference on Advances in Artificial Intelligence, London, UK.","DOI":"10.1145\/3441417.3441431"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1109\/TASLP.2016.2592698","article-title":"Automatic environmental sound recognition: Performance versus computational cost","volume":"24","author":"Sigtia","year":"2016","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"2200","DOI":"10.1109\/TASLP.2016.2599275","article-title":"A probabilistic modeling approach to hearing loss compensation","volume":"24","year":"2016","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1109\/TASLP.2018.2860786","article-title":"Learning-based reference-free speech quality measures for hearing aid applications","volume":"26","author":"Salehi","year":"2018","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"66529","DOI":"10.1109\/ACCESS.2020.2984903","article-title":"A new deep CNN model for environmental sound classification","volume":"8","author":"Demir","year":"2020","journal-title":"IEEE Access"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Ridha, A.M., and Shehieb, W. (2021, January 12\u201317). Assistive Technology for Hearing-Impaired and Deaf Students Utilizing Augmented Reality. Proceedings of the 2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Virtual Conference.","DOI":"10.1109\/CCECE53047.2021.9569193"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Ayu, A.I.S.M., and Karyono, K.K. (2014, January 10\u201312). Audio detection (Audition): Android based sound detection application for hearing-impaired using AdaBoostM1 classifier with REPTree weaklearner. Proceedings of the 2014 Asia-Pacific Conference on Computer Aided System Engineering (APCASE), South Kuta, Indonesia.","DOI":"10.1109\/APCASE.2014.6924487"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Chen, C.Y., Kuo, P.Y., Chiang, Y.H., Liang, J.Y., Liang, K.W., and Chang, P.C. (2019, January 15\u201318). Audio-Based Early Warning System of Sound Events on the Road for Improving the Safety of Hearing-Impaired People. Proceedings of the 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), OSAKA, Japan.","DOI":"10.1109\/GCCE46687.2019.9015516"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Bhat, G.S., Shankar, N., and Panahi, I.M. (2020, January 20\u201324). Automated machine learning based speech classification for hearing aid applications and its real-time implementation on smartphone. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175693"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Healy, E.W., and Yoho, S.E. (2016, January 16\u201320). Difficulty understanding speech in noise by the hearing impaired: Underlying causes and technological solutions. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590647"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Jatturas, C., Chokkoedsakul, S., Ayudhya, P.D.N., Pankaew, S., Sopavanit, C., and Asdornwised, W. (2019, January 10\u201313). Recurrent Neural Networks for Environmental Sound Recognition using Scikit-learn and Tensorflow. Proceedings of the 2019 16th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Pattaya, Thailand.","DOI":"10.1109\/ECTI-CON47248.2019.8955382"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Saleem, N., Khattak, M.I., Ahmad, S., Ali, M.Y., and Mohmand, M.I. (2020, January 14\u201318). Machine Learning Approach for Improving the Intelligibility of Noisy Speech. Proceedings of the 2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan.","DOI":"10.1109\/IBCAST47879.2020.9044553"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Davis, N., and Suresh, K. (2018, January 6\u20138). Environmental sound classification using deep convolutional neural networks and data augmentation. Proceedings of the 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS), Thiruvananthapuram, India.","DOI":"10.1109\/RAICS.2018.8635051"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1109\/TASL.2009.2017438","article-title":"Environmental sound recognition with time\u2013frequency audio features","volume":"17","author":"Chu","year":"2009","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Chu, S., Narayanan, S., Kuo, C.C.J., and Mataric, M.J. (2006, January 9\u201312). Where Am I?. Scene recognition for mobile robots using audio features. In Proceedings of the 2006 IEEE International Conference on Multimedia and Expo, Toronto, ON, Canada.","DOI":"10.1109\/ICME.2006.262661"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"124055","DOI":"10.1109\/ACCESS.2020.3006082","article-title":"Hybrid computerized method for environmental sound classification","volume":"8","author":"Ullo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zou, Y., and Shi, W. (2017, January 23\u201325). Dilated convolution neural network with LeakyReLU for environmental sound classification. Proceedings of the 2017 22nd International Conference on Digital Signal Processing (DSP), London, UK.","DOI":"10.1109\/ICDSP.2017.8096153"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Piczak, K.J. (2015, January 17\u201320). Environmental sound classification with convolutional neural networks. Proceedings of the 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), Boston, MA, USA.","DOI":"10.1109\/MLSP.2015.7324337"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Han, B.j., and Hwang, E. (July, January 28). Environmental sound classification based on feature collaboration. Proceedings of the 2009 IEEE International Conference on Multimedia and Expo, New York, NY, USA.","DOI":"10.1109\/ICME.2009.5202553"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/TASE.2013.2285131","article-title":"Gabor-based nonuniform scale-frequency map for environmental sound classification in home automation","volume":"11","author":"Wang","year":"2013","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1109\/LSP.2017.2657381","article-title":"Deep convolutional neural networks and data augmentation for environmental sound classification","volume":"24","author":"Salamon","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_136","unstructured":"Wang, J.C., Wang, J.F., He, K.W., and Hsu, C.S. (2006, January 16\u201321). Environmental sound classification using hybrid SVM\/KNN classifier and MPEG-7 audio low-level descriptor. Proceedings of the 2006 IEEE International Joint Conference on Neural Network Proceedings, Vancouver, BC, Canada."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1097\/AUD.0000000000000649","article-title":"Machine learning models for the hearing impairment prediction in workers exposed to complex industrial noise: A pilot study","volume":"40","author":"Zhao","year":"2019","journal-title":"Ear Hear."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Tokozume, Y., and Harada, T. (2017, January 5\u20139). Learning environmental sounds with end-to-end convolutional neural network. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952651"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.aej.2019.05.006","article-title":"Enhanced smart hearing aid using deep neural networks","volume":"58","author":"Nossier","year":"2019","journal-title":"Alex. Eng. J."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.eswa.2019.06.040","article-title":"End-to-end environmental sound classification using a 1D convolutional neural network","volume":"136","author":"Abdoli","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"107389","DOI":"10.1016\/j.apacoust.2020.107389","article-title":"Environmental sound classification using a regularized deep convolutional neural network with data augmentation","volume":"167","author":"Mushtaq","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.apacoust.2018.12.019","article-title":"Environmental sound classification with dilated convolutions","volume":"148","author":"Chen","year":"2019","journal-title":"Appl. Acoust."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"107581","DOI":"10.1016\/j.apacoust.2020.107581","article-title":"Spectral images based environmental sound classification using CNN with meaningful data augmentation","volume":"172","author":"Mushtaq","year":"2021","journal-title":"Appl. Acoust."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"122613","DOI":"10.1016\/j.physa.2019.122613","article-title":"Environmental sound classification using optimum allocation sampling based empirical mode decomposition","volume":"537","author":"Ahmad","year":"2020","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Medhat, F., Chesmore, D., and Robinson, J. (2017). Masked conditional neural networks for environmental sound classification. International Conference on Innovative Techniques and Applications of Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-319-71078-5_2"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Xu, S., Cao, S., and Zhang, S. (2018). Deep convolutional neural network with mixup for environmental sound classification. Chinese Conference on Pattern Recognition and Computer Vision (PRCV), Springer.","DOI":"10.1007\/978-3-030-03335-4_31"},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Sailor, H.B., Agrawal, D.M., and Patil, H.A. (2017, January 20\u201324). Unsupervised Filterbank Learning Using Convolutional Restricted Boltzmann Machine for Environmental Sound Classification. Proceedings of the INTERSPEECH 2017, Stockholm, Sweden.","DOI":"10.21437\/Interspeech.2017-831"},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Sharma, J., Granmo, O.C., and Goodwin, M. (2020, January 25\u201329). Environment Sound Classification Using Multiple Feature Channels and Attention Based Deep Convolutional Neural Network. Proceedings of the INTERSPEECH 2020, Shanghai, China.","DOI":"10.21437\/Interspeech.2020-1303"},{"key":"ref_149","unstructured":"Mohaimenuzzaman, M., Bergmeir, C., West, I.T., and Meyer, B. (2021). Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices. arXiv."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"3978","DOI":"10.1109\/TMM.2020.3035275","article-title":"Environmental sound classification using local binary pattern and audio features collaboration","volume":"23","author":"Toffa","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"7717","DOI":"10.1109\/ACCESS.2018.2888882","article-title":"Sound classification using convolutional neural network and tensor deep stacking network","volume":"7","author":"Khamparia","year":"2019","journal-title":"IEEE Access"},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Su, Y., Zhang, K., Wang, J., and Madani, K. (2019). Environment sound classification using a two-stream CNN based on decision-level fusion. Sensors, 19.","DOI":"10.3390\/s19071733"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Bragg, D., Huynh, N., and Ladner, R.E. (2016, January 23\u201326). A personalizable mobile sound detector app design for deaf and hard-of-hearing users. Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility, Reno, NV, USA.","DOI":"10.1145\/2982142.2982171"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Jatturas, C., Chokkoedsakul, S., Avudhva, P.D.N., Pankaew, S., Sopavanit, C., and Asdornwised, W. (2019, January 12\u201314). Feature-based and Deep Learning-based Classification of Environmental Sound. Proceedings of the 2019 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), Bangkok, Thailand.","DOI":"10.1109\/ICCE-Asia46551.2019.8942209"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s00779-005-0045-4","article-title":"Acoustic environment as an indicator of social and physical context","volume":"10","author":"Smith","year":"2006","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Ma, L., Smith, D.J., and Milner, B.P. (2003, January 1\u20134). Context awareness using environmental noise classification. Proceedings of the INTERSPEECH, Geneva, Switzerland.","DOI":"10.21437\/Eurospeech.2003-626"},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1109\/TASSP.1977.1162950","article-title":"Short term spectral analysis, synthesis, and modification by discrete Fourier transform","volume":"25","author":"Allen","year":"1977","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"1558","DOI":"10.1109\/PROC.1977.10770","article-title":"A unified approach to short-time Fourier analysis and synthesis","volume":"65","author":"Allen","year":"1977","journal-title":"Proc. IEEE"},{"key":"ref_159","unstructured":"Allen, J. (1982, January 3\u20135). Applications of the short time Fourier transform to speech processing and spectral analysis. Proceedings of the ICASSP\u201982. IEEE International Conference on Acoustics, Speech, and Signal Processing, Paris, France."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1109\/TMM.2009.2017607","article-title":"Text-like segmentation of general audio for content-based retrieval","volume":"11","author":"Lu","year":"2009","journal-title":"IEEE Trans. Multimed."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8361\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:07:00Z","timestamp":1760144820000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8361"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,31]]},"references-count":160,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218361"],"URL":"https:\/\/doi.org\/10.3390\/s22218361","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,31]]}}}