{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T06:56:15Z","timestamp":1770101775689,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-026-21229-2","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T18:51:35Z","timestamp":1770058295000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-head attention based feature fusion for Underwater Acoustic Target Recognition(UATR)"],"prefix":"10.1007","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4163-7282","authenticated-orcid":false,"given":"Rashid","family":"Nadeem","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9414-7483","authenticated-orcid":false,"given":"Aswathi","family":"Mohan P. P.","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7257-7920","authenticated-orcid":false,"given":"V.","family":"Uma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"key":"21229_CR1","doi-asserted-by":"publisher","unstructured":"Tang J, Ma E, Qu Y, Gao W, Zhang Y, Gan L (2025) UAPT: An Underwater Acoustic Target Recognition Method Based on Pre- Trained Transformer. https:\/\/doi.org\/10.21203\/rs.3.rs-4253542\/v1","DOI":"10.21203\/rs.3.rs-4253542\/v1"},{"key":"21229_CR2","doi-asserted-by":"publisher","unstructured":"Zhao Y, Xie G, Chen H, Chen M, Huang L (2025) Enhancing underwater acoustic target recognition through advanced feature fusion and deep learning. J Marine Sci Eng 13:278. https:\/\/doi.org\/10.3390\/jmse13020278","DOI":"10.3390\/jmse13020278"},{"key":"21229_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3233\/JIFS-234876","volume":"46","author":"H Lei","year":"2023","unstructured":"Lei H, Li D, Jiang H (2023) Multi-feature fusion sonar image target detection evaluation based on particle swarm optimization algorithm. J Intell Fuzzy Syst 46:1\u201313. https:\/\/doi.org\/10.3233\/JIFS-234876","journal-title":"J Intell Fuzzy Syst"},{"key":"21229_CR4","doi-asserted-by":"publisher","unstructured":"Yin Z, Zhang S, Sun R, Ding Y, Guo Y (2023) Sonar image target detection based on deep learning. In: 2023 International conference on distributed computing and electrical circuits and electronics (ICDCECE), pp 1\u20139. https:\/\/doi.org\/10.1109\/ICDCECE57866.2023.10150970","DOI":"10.1109\/ICDCECE57866.2023.10150970"},{"key":"21229_CR5","doi-asserted-by":"publisher","unstructured":"Fang H, Zhang X, Xu J (2023) Underwater acoustic target identification via siamese network. In: OCEANS 2023 - Limerick, pp 1\u20134. https:\/\/doi.org\/10.1109\/OCEANSLimerick52467.2023.10244292","DOI":"10.1109\/OCEANSLimerick52467.2023.10244292"},{"key":"21229_CR6","doi-asserted-by":"publisher","unstructured":"Liu S, Fu X, Xu H, Zhang J, Zhang A, Zhou Q, Zhang H (2023) A fine-grained ship-radiated noise recognition system using deep hybrid neural networks with multi-scale features. Remote Sensing 15(8). https:\/\/doi.org\/10.3390\/rs15082068","DOI":"10.3390\/rs15082068"},{"key":"21229_CR7","doi-asserted-by":"publisher","first-page":"88917","DOI":"10.1109\/ACCESS.2025.3569344","volume":"13","author":"JS Walia","year":"2025","unstructured":"Walia JS, Haridass K, Pavithra LK (2025) Deep learning innovations for underwater waste detection: An in-depth analysis. IEEE Access 13:88917\u201388929. https:\/\/doi.org\/10.1109\/ACCESS.2025.3569344","journal-title":"IEEE Access"},{"key":"21229_CR8","doi-asserted-by":"publisher","unstructured":"Pu Z, Zhang Q, Xue Y, Zhu P, Cui X (2024) A novel multi-feature fusion model based on pre-trained wav2vec 2.0 for underwater acoustic target recognition. Remote Sensing 16(13). https:\/\/doi.org\/10.3390\/rs16132442","DOI":"10.3390\/rs16132442"},{"key":"21229_CR9","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.1121\/1.5067735","volume":"144","author":"R Sabara","year":"2018","unstructured":"Sabara R, Jesus S (2018) Underwater acoustic target recognition using graph convolutional neural networks. J Acoust Soc Am 144:1744\u20131744. https:\/\/doi.org\/10.1121\/1.5067735","journal-title":"J Acoust Soc Am"},{"key":"21229_CR10","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1080\/10584587.2021.1911338","volume":"218","author":"W Wang","year":"2021","unstructured":"Wang W, Zhao X, Liu D (2021) Design and optimization of 1d-cnn for spectrum recognition of underwater targets. Integr Ferroelectr 218:164\u2013179. https:\/\/doi.org\/10.1080\/10584587.2021.1911338","journal-title":"Integr Ferroelectr"},{"key":"21229_CR11","doi-asserted-by":"publisher","unstructured":"Yang H, Xu G, Yi S, Li Y (2019) A new cooperative deep learning method for underwater acoustic target recognition. In: OCEANS 2019 - Marseille, pp 1\u20134. https:\/\/doi.org\/10.1109\/OCEANSE.2019.8867490","DOI":"10.1109\/OCEANSE.2019.8867490"},{"key":"21229_CR12","doi-asserted-by":"publisher","unstructured":"Wei Z, Ju Y, Song M (2018) A method of underwater acoustic signal classification based on deep neural network. In: 2018 5th International conference on information science and control engineering (ICISCE), pp 46\u201350. https:\/\/doi.org\/10.1109\/ICISCE.2018.00019","DOI":"10.1109\/ICISCE.2018.00019"},{"key":"21229_CR13","doi-asserted-by":"publisher","unstructured":"Gemmeke JF, Ellis DPW, Freedman D, Jansen A, Lawrence W, Moore RC, Plakal M, Ritter M (2017) Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp 776\u2013780. https:\/\/doi.org\/10.1109\/ICASSP.2017.7952261","DOI":"10.1109\/ICASSP.2017.7952261"},{"key":"21229_CR14","doi-asserted-by":"publisher","unstructured":"Yao H, Gao T, Wang Y, Wang H, Chen X (2024) Mobile_vit: Underwater acoustic target recognition method based on local\u2013global feature fusion. J Marine Sci Eng 12(4). https:\/\/doi.org\/10.3390\/jmse12040589","DOI":"10.3390\/jmse12040589"},{"key":"21229_CR15","doi-asserted-by":"crossref","unstructured":"PP AM, Uma V (2023) Fetal hypoxia detection using ctg signals and cnn models. Int Res J Adv Sci Hub 5(05):434\u2013441","DOI":"10.47392\/irjash.2023.S059"},{"key":"21229_CR16","doi-asserted-by":"publisher","unstructured":"Wang X, Liu A, Zhang Y, Xue F (2019) Underwater acoustic target recognition: A combination of multi-dimensional fusion features and modified deep neural network. Remote Sensing 11(16). https:\/\/doi.org\/10.3390\/rs11161888","DOI":"10.3390\/rs11161888"},{"key":"21229_CR17","doi-asserted-by":"publisher","unstructured":"Yao Q, Wang Y, Yang Y (2023) Underwater acoustic target recognition based on data augmentation and residual cnn. Electronics 12(5). https:\/\/doi.org\/10.3390\/electronics12051206","DOI":"10.3390\/electronics12051206"},{"key":"21229_CR18","doi-asserted-by":"publisher","unstructured":"Sun B, Luo X (2023) Underwater acoustic target recognition based on automatic feature and contrastive coding. IET Radar Sonar & Navigation 17. https:\/\/doi.org\/10.1049\/rsn2.12418","DOI":"10.1049\/rsn2.12418"},{"key":"21229_CR19","doi-asserted-by":"publisher","unstructured":"Li Z, Xiang S, Yu T, Gao J, Ruan J, Hu Y, Liu T, Fu Y (2024) Oceanship: A large-scale dataset for underwater audio target recognition, pp 475\u2013486. https:\/\/doi.org\/10.1007\/978-981-97-5591-2_40","DOI":"10.1007\/978-981-97-5591-2_40"},{"key":"21229_CR20","doi-asserted-by":"publisher","unstructured":"Liu D, Yang H, Hou W, Wang B (2024) A novel underwater acoustic target recognition method based on mfcc and racnn. Sensors 24(1). https:\/\/doi.org\/10.3390\/s24010273","DOI":"10.3390\/s24010273"},{"key":"21229_CR21","doi-asserted-by":"publisher","unstructured":"Ahmad F, Ansari MZ, Anwar R, Shahzad B, Ikram A (2024) Deep learning based classification of underwater acoustic signals. Procedia Comput Sci 235:1115\u20131124. https:\/\/doi.org\/10.1016\/j.procs.2024.04.106. International Conference on Machine Learning and Data Engineering (ICMLDE 2023)","DOI":"10.1016\/j.procs.2024.04.106"},{"key":"21229_CR22","doi-asserted-by":"publisher","unstructured":"Feng S, Zhu X, Ma S, Lan Q (2023) Adversarial attacks in underwater acoustic target recognition with deep learning models. Remote Sensing 15(22). https:\/\/doi.org\/10.3390\/rs15225386","DOI":"10.3390\/rs15225386"},{"key":"21229_CR23","doi-asserted-by":"publisher","unstructured":"Sunilkumar A, Joseph\u00a0KS, Kumar\u00a0KM (2023) Underwater passive target classification based on $$\\beta$$ variational autoencoder and mfcc. In: 2023 Sensor Signal Processing for Defence Conference (SSPD), pp 1\u20135. https:\/\/doi.org\/10.1109\/SSPD57945.2023.10256865","DOI":"10.1109\/SSPD57945.2023.10256865"},{"key":"21229_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115270","volume":"183","author":"M Irfan","year":"2021","unstructured":"Irfan M, Jiangbin Z, Ali S, Iqbal M, Masood Z, Hamid U (2021) Deepship: An underwater acoustic benchmark dataset and a separable convolution based autoencoder for classification. Expert Syst Appl 183:115270. https:\/\/doi.org\/10.1016\/j.eswa.2021.115270","journal-title":"Expert Syst Appl"},{"key":"21229_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/1214301","volume":"2018","author":"H Gang","year":"2018","unstructured":"Gang H, Wang K, Peng Y, Qiu M, Shi J, Liu L (2018) Deep learning methods for underwater target feature extraction and recognition. Comput Intell Neurosci 2018:1\u201310. https:\/\/doi.org\/10.1155\/2018\/1214301","journal-title":"Comput Intell Neurosci"},{"key":"21229_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2024.119841","volume":"315","author":"L Chen","year":"2025","unstructured":"Chen L, Luo X, Zhou H, Shen Q, Chen L, Huan C (2025) Underwater acoustic multi-target recognition based on channel attention mechanism. Ocean Eng 315:119841. https:\/\/doi.org\/10.1016\/j.oceaneng.2024.119841","journal-title":"Ocean Eng"},{"issue":"11","key":"21229_CR27","doi-asserted-by":"publisher","first-page":"1829","DOI":"10.1109\/29.103085","volume":"38","author":"B Boashash","year":"1990","unstructured":"Boashash B, O\u2019Shea P (1990) A methodology for detection and classification of some underwater acoustic signals using time-frequency analysis techniques. IEEE Trans Acoust Speech Signal Process 38(11):1829\u20131841. https:\/\/doi.org\/10.1109\/29.103085","journal-title":"IEEE Trans Acoust Speech Signal Process"},{"key":"21229_CR28","doi-asserted-by":"publisher","unstructured":"Huang Z, Wan Q, Xiong Z (2024) Underwater target recognition based on multi-feature fusion and attention mechanism. In: 2024 IEEE International conference on unmanned systems (ICUS), pp 1120\u20131125. https:\/\/doi.org\/10.1109\/ICUS61736.2024.10839852","DOI":"10.1109\/ICUS61736.2024.10839852"},{"key":"21229_CR29","doi-asserted-by":"publisher","unstructured":"Dong W, Fu J, Wang X, Shen Z (2024) Underwater acoustic target feature fusion recognition using multitask learning. In: 2024 OES China Ocean Acoustics (COA), pp 1\u20135. https:\/\/doi.org\/10.1109\/COA58979.2024.10723455","DOI":"10.1109\/COA58979.2024.10723455"},{"key":"21229_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2024.117305","volume":"299","author":"X Wang","year":"2024","unstructured":"Wang X, Wu P, Li B, Zhan G, Liu J, Liu Z (2024) A self-supervised dual-channel self-attention acoustic encoder for underwater acoustic target recognition. Ocean Eng 299:117305. https:\/\/doi.org\/10.1016\/j.oceaneng.2024.117305","journal-title":"Ocean Eng"},{"key":"21229_CR31","doi-asserted-by":"publisher","unstructured":"Ma G, Shen X, Yan Y, Yao H, Wang H (2025) Energy state sensing for robust mac protocol identification in underwater acoustic networks. IEEE Trans Cognit Commun Netw 1\u20131. https:\/\/doi.org\/10.1109\/TCCN.2025.3561297","DOI":"10.1109\/TCCN.2025.3561297"},{"key":"21229_CR32","doi-asserted-by":"publisher","unstructured":"Aswathi\u00a0Mohan PP, Uma V (2024) Enhanced prediction of fetal hypoxia through gan-synthesized data and advanced ensemble feature fusion. In: 2024 15th International conference on computing communication and networking technologies (ICCCNT), pp 1\u20137. https:\/\/doi.org\/10.1109\/ICCCNT61001.2024.10724351","DOI":"10.1109\/ICCCNT61001.2024.10724351"},{"key":"21229_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2025.105259","volume":"164","author":"PP Aswathi Mohan","year":"2025","unstructured":"Aswathi Mohan PP, Uma V, Rengaraj S, Hamsika V (2025) Fhr signal analysis using attention-based 1dcnn-bilstm neural network for intrapartum fetal monitoring. Digital Signal Processing 164:105259. https:\/\/doi.org\/10.1016\/j.dsp.2025.105259","journal-title":"Digital Signal Processing"},{"key":"21229_CR34","doi-asserted-by":"publisher","unstructured":"Cao Y, Yan J, Sun K, Luo X (2023) Hydroacoustic target detection based on improved gfcc and lightweight neural network, pp 6239\u20136243. https:\/\/doi.org\/10.23919\/CCC58697.2023.10239921","DOI":"10.23919\/CCC58697.2023.10239921"},{"key":"21229_CR35","doi-asserted-by":"publisher","unstructured":"Nadeem R, Sivakumar T (2023) Flight fare forecasting: A machine learning approach to predict ticket prices, 703\u2013713. https:\/\/doi.org\/10.1007\/978-981-99-3878-0_60","DOI":"10.1007\/978-981-99-3878-0_60"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21229-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-026-21229-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21229-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T18:51:36Z","timestamp":1770058296000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-026-21229-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,2]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["21229"],"URL":"https:\/\/doi.org\/10.1007\/s11042-026-21229-2","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,2]]},"assertion":[{"value":"26 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethical guidelines were followed.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"This study does not involve research with human participants conducted by the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"All authors have approved the manuscript and consented to its submission, with necessary institutional permissions obtained.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Submit"}},{"value":"The authors declare no competing interests that could have influenced the results or interpretations of this study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"99"}}