{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T02:03:16Z","timestamp":1780020196556,"version":"3.53.1"},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Signal Processing"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.sigpro.2026.110668","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T15:16:35Z","timestamp":1776957395000},"page":"110668","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Deep attention mechanism residual network for audio deepfake detection using multi scale cepstral coefficient features"],"prefix":"10.1016","volume":"247","author":[{"given":"Haitao","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingzhuo","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiai","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongliang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huapeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.sigpro.2026.110668_bib0001","series-title":"Diffusion Model in Modern detection: Advancing Deepfake Techniques","author":"Siddiqui","year":"2025"},{"key":"10.1016\/j.sigpro.2026.110668_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.102993","article-title":"Advances in DeepFake detection algorithms: exploring fusion techniques in single and multi-modal approach","author":"Kumar","year":"2025","journal-title":"Inf. Fusion."},{"key":"10.1016\/j.sigpro.2026.110668_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.sigpro.2024.109822","article-title":"DSM: domain Shift modeling for general deepfake detection","volume":"230","author":"Zhang","year":"2025","journal-title":"Signal Process."},{"issue":"8","key":"10.1016\/j.sigpro.2026.110668_bib0004","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.13322","article-title":"Review of audio deepfake detection techniques: issues and prospects","volume":"40","author":"Dixit","year":"2023","journal-title":"Expert. Syst."},{"key":"10.1016\/j.sigpro.2026.110668_bib0005","series-title":"Proceedings of the 1st International Workshop on Deepfake Detection for Audio Multimedia","first-page":"85","article-title":"Human perception of audio deepfakes","author":"M\u00fcller","year":"2022"},{"issue":"2","key":"10.1016\/j.sigpro.2026.110668_bib0006","doi-asserted-by":"crossref","DOI":"10.1002\/widm.1520","article-title":"Deepfake detection using deep learning methods: a systematic and comprehensive review","volume":"14","author":"Heidari","year":"2024","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"10.1016\/j.sigpro.2026.110668_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.116770","article-title":"Voice spoofing detector: a unified anti-spoofing framework","volume":"198","author":"Javed","year":"2022","journal-title":"Expert. Syst. Appl."},{"issue":"7810","key":"10.1016\/j.sigpro.2026.110668_bib0008","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1038\/s41586-020-2314-9","article-title":"Variability in the analysis of a single neuroimaging dataset by many teams","volume":"582","author":"Botvinik-Nezer","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.sigpro.2026.110668_bib0009","series-title":"2014 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"4002","article-title":"Inter dataset variability compensation for speaker recognition","author":"Aronowitz","year":"2014"},{"issue":"2","key":"10.1016\/j.sigpro.2026.110668_bib0010","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.ipm.2016.10.003","article-title":"Balancing between over-weighting and under-weighting in supervised term weighting","volume":"53","author":"Wu","year":"2017","journal-title":"Inf. Process. Manag."},{"issue":"6","key":"10.1016\/j.sigpro.2026.110668_bib0011","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s10462-024-10810-6","article-title":"Deepfake video detection: challenges and opportunities","volume":"57","author":"Kaur","year":"2024","journal-title":"Artif. Intell. Rev."},{"issue":"6","key":"10.1016\/j.sigpro.2026.110668_bib0012","first-page":"55","article-title":"Synthetic speech spoofing detection using MFCC and radial basis function SVM","volume":"8","author":"Bhangale","year":"2018","journal-title":"IOSR J. Eng."},{"key":"10.1016\/j.sigpro.2026.110668_bib0013","doi-asserted-by":"crossref","unstructured":"Kawa, P., Plata M., and Syga P. \"Attack agnostic dataset: towards generalization and stabilization of audio deepfake detection\". arXiv preprint arXiv:2206.13979 (2022).","DOI":"10.21437\/Interspeech.2022-10078"},{"key":"10.1016\/j.sigpro.2026.110668_bib0014","first-page":"2062","article-title":"Combining evidences from Mel cepstral, cochlear filter cepstral and instantaneous frequency features for detection of natural vs. spoofed speech","author":"Patel","year":"2015","journal-title":"Interspeech"},{"key":"10.1016\/j.sigpro.2026.110668_bib0015","doi-asserted-by":"crossref","unstructured":"Alzantot, M., Wang Z., and Srivastava M.B.. \"Deep residual neural networks for audio spoofing detection\". arXiv preprint arXiv:1907.00501 (2019).","DOI":"10.21437\/Interspeech.2019-3174"},{"issue":"10","key":"10.1016\/j.sigpro.2026.110668_bib0016","doi-asserted-by":"crossref","first-page":"4633","DOI":"10.1109\/TNNLS.2017.2771947","article-title":"Spoofing detection in automatic speaker verification systems using DNN classifiers and dynamic acoustic features","volume":"29","author":"Yu","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"9","key":"10.1016\/j.sigpro.2026.110668_bib0017","doi-asserted-by":"crossref","first-page":"7300","DOI":"10.1016\/j.jksuci.2022.02.024","article-title":"A robust voice spoofing detection system using novel CLS-LBP features and LSTM","volume":"34","author":"Dawood","year":"2022","journal-title":"J. King Saud Univ..Comput. Inf. Sci."},{"issue":"5","key":"10.1016\/j.sigpro.2026.110668_bib0018","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1109\/JSTSP.2020.2999185","article-title":"Recurrent convolutional structures for audio spoof and video deepfake detection","volume":"14","author":"Chintha","year":"2020","journal-title":"IEEE J. Sel. Top. Signal. Process."},{"key":"10.1016\/j.sigpro.2026.110668_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123941","article-title":"Audio-deepfake detection: adversarial attacks and countermeasures","volume":"250","author":"Rabhi","year":"2024","journal-title":"Expert. Syst. Appl."},{"issue":"2\u20133","key":"10.1016\/j.sigpro.2026.110668_bib0020","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/0167-6393(92)90012-V","article-title":"Voice transformation using PSOLA technique","volume":"11","author":"Valbret","year":"1992","journal-title":"Speech. Commun."},{"key":"10.1016\/j.sigpro.2026.110668_bib0021","article-title":"Transformer for authenticating the source microphone in digital audio forensics","volume":"45","author":"Qamhan","year":"2023","journal-title":"Forensic Sci. Int. Digit. Investig."},{"key":"10.1016\/j.sigpro.2026.110668_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.forsciint.2021.110702","article-title":"A method of forensic authentication of audio recordings generated using the Voice Memos application in the iPhone","volume":"320","author":"Park","year":"2021","journal-title":"Forensic Sci. Int."},{"key":"10.1016\/j.sigpro.2026.110668_bib0023","doi-asserted-by":"crossref","first-page":"2994","DOI":"10.1109\/ACCESS.2017.2672681","article-title":"An automatic digital audio authentication\/forensics system","volume":"5","author":"Ali","year":"2017","journal-title":"IEEE Access."},{"issue":"2","key":"10.1016\/j.sigpro.2026.110668_bib0024","doi-asserted-by":"crossref","DOI":"10.1561\/116.00000017","article-title":"An application-oriented taxonomy on spoofing, disguise and countermeasures in speaker recognition","volume":"11","author":"Li","year":"2022","journal-title":"APSIPa Trans. Signal. Inf. Process."},{"key":"10.1016\/j.sigpro.2026.110668_bib0025","article-title":"Spoofing detection with DNN and one-class SVM for the ASVspoof 2015 challenge","volume":"2015","author":"Villalba","year":"2015","journal-title":"Proc. Interspeech. Vol."},{"key":"10.1016\/j.sigpro.2026.110668_bib0026","series-title":"2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","article-title":"Recognition of spoofed voice using convolutional neural networks","author":"Liang","year":"2017"},{"key":"10.1016\/j.sigpro.2026.110668_bib0027","first-page":"82","article-title":"Audio replay attack detection with deep learning frameworks","author":"Lavrentyeva","year":"2017","journal-title":"Interspeech"},{"key":"10.1016\/j.sigpro.2026.110668_bib0028","series-title":"2017 International Joint Conference on Neural Networks (IJCNN)","first-page":"3483","article-title":"On the use of deep recurrent neural networks for detecting audio spoofing attacks","author":"Scardapane","year":"2017"},{"key":"10.1016\/j.sigpro.2026.110668_bib0029","first-page":"676","article-title":"A deep identity representation for noise robust spoofing detection","author":"Gomez-Alanis","year":"2018","journal-title":"Proc. Interspeech. Vol."},{"key":"10.1016\/j.sigpro.2026.110668_bib0030","first-page":"1068","article-title":"A light convolutional GRU-RNN deep feature extractor for ASV spoofing detection","volume":"2019","author":"Gomez-Alanis","year":"2019","journal-title":"Proc. Interspeech. Vol."},{"key":"10.1016\/j.sigpro.2026.110668_bib0031","series-title":"2018 IEEE international conference on acoustics, speech and signal processing (ICASSP)","first-page":"2052","article-title":"Recurrent neural networks for automatic replay spoofing attack detection","author":"Chen","year":"2018"},{"key":"10.1016\/j.sigpro.2026.110668_bib0032","series-title":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"4860","article-title":"End-to-end spoofing detection with raw waveform CLDNNS","author":"Dinkel","year":"2017"},{"key":"10.1016\/j.sigpro.2026.110668_bib0033","series-title":"ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"6369","article-title":"End-to-end anti-spoofing with rawnet2","author":"Tak","year":"2021"},{"key":"10.1016\/j.sigpro.2026.110668_bib0034","series-title":"ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"6359","article-title":"A capsule network based approach for detection of audio spoofing attacks","author":"Luo","year":"2021"},{"key":"10.1016\/j.sigpro.2026.110668_bib0035","doi-asserted-by":"crossref","unstructured":"Tak, H., et al. \"Graph attention networks for anti-spoofing\". arXiv preprint arXiv:2104.03654 (2021).","DOI":"10.21437\/Interspeech.2021-993"},{"issue":"10","key":"10.1016\/j.sigpro.2026.110668_bib0036","doi-asserted-by":"crossref","first-page":"1702","DOI":"10.1109\/TASLP.2018.2842159","article-title":"Supervised speech separation based on deep learning: an overview","volume":"26","author":"Wang","year":"2018","journal-title":"IEEE\/ACM. Trans. Audio Speech. Lang. Process."},{"key":"10.1016\/j.sigpro.2026.110668_bib0037","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"3","article-title":"Cbam: convolutional block attention module","author":"Woo","year":"2018"},{"key":"10.1016\/j.sigpro.2026.110668_bib0038","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"2","key":"10.1016\/j.sigpro.2026.110668_bib0039","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","article-title":"Res2net: a new multi-scale backbone architecture","volume":"43","author":"Gao","year":"2019","journal-title":"IEEE Trans. Pattern. Anal. Mach. Intell."},{"key":"10.1016\/j.sigpro.2026.110668_bib0040","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1492","article-title":"Aggregated residual transformations for deep neural networks","author":"Xie","year":"2017"},{"key":"10.1016\/j.sigpro.2026.110668_bib0041","series-title":"ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"13131","article-title":"A robust audio deepfake detection system via multi-view feature","author":"Yang","year":"2024"},{"key":"10.1016\/j.sigpro.2026.110668_bib0042","article-title":"Leak detection of underground water pipelines using acoustic feature extraction","author":"Zhao","year":"2025","journal-title":"IEEE Internet. Things. J."},{"issue":"4","key":"10.1016\/j.sigpro.2026.110668_bib0043","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/JSTSP.2016.2647201","article-title":"Cochlear filter and instantaneous frequency based features for spoofed speech detection","volume":"11","author":"Patel","year":"2016","journal-title":"IEEE J. Sel. Top. Signal. Process."},{"key":"10.1016\/j.sigpro.2026.110668_bib0044","doi-asserted-by":"crossref","first-page":"8906","DOI":"10.1109\/TMM.2023.3243616","article-title":"Dilateformer: multi-scale dilated transformer for visual recognition","volume":"25","author":"Jiao","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.sigpro.2026.110668_bib0045","doi-asserted-by":"crossref","unstructured":"Todisco, M., et al. \"ASVspoof 2019: future horizons in spoofed and fake audio detection\". arXiv preprint arXiv:1904.05441 (2019).","DOI":"10.21437\/Interspeech.2019-2249"},{"key":"10.1016\/j.sigpro.2026.110668_bib0046","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1109\/TASLP.2023.3285283","article-title":"Asvspoof 2021: towards spoofed and deepfake speech detection in the wild","volume":"31","author":"Liu","year":"2023","journal-title":"IEEE\/ACM. Trans. Audio Speech. Lang. Process."},{"issue":"8","key":"10.1016\/j.sigpro.2026.110668_bib0047","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern. Recognit. Lett."},{"key":"10.1016\/j.sigpro.2026.110668_bib0048","series-title":"2022 IEEE International Joint Conference on Biometrics (IJCB)","first-page":"1","article-title":"Statnet: spectral and temporal features based multi-task network for audio spoofing detection","author":"Ranjan","year":"2022"},{"key":"10.1016\/j.sigpro.2026.110668_bib0049","series-title":"2022 IEEE International Workshop on Information Forensics and Security (WIFS)","first-page":"1","article-title":"Audio deepfake detectionby speaker verification","author":"Pianese","year":"2022"},{"key":"10.1016\/j.sigpro.2026.110668_bib0050","doi-asserted-by":"crossref","unstructured":"Kawa, P., et al. \"Improved deepfake detection using whisper features\". arXiv preprint arXiv:2306.01428 (2023).","DOI":"10.21437\/Interspeech.2023-1537"},{"key":"10.1016\/j.sigpro.2026.110668_bib0051","series-title":"ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"10761","article-title":"Frame-to-utterance convergence: a spectra-temporal approach for unified spoofing detection","author":"Khan","year":"2024"},{"key":"10.1016\/j.sigpro.2026.110668_bib0052","series-title":"ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1406","article-title":"Multi-scale permutation entropy for audio deepfake detection","author":"Wang","year":"2024"},{"key":"10.1016\/j.sigpro.2026.110668_bib0053","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2024.104145","article-title":"Acoustic features analysis for explainable machine learning-based audio spoofing detection","volume":"249","author":"Bisogni","year":"2024","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.sigpro.2026.110668_bib0054","doi-asserted-by":"crossref","unstructured":"Lai, C.-I., et al. \"ASSERT: anti-spoofing with squeeze-excitation and residual networks\". arXiv preprint arXiv:1904.01120 (2019).","DOI":"10.21437\/Interspeech.2019-1794"},{"key":"10.1016\/j.sigpro.2026.110668_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.sysarc.2023.102981","article-title":"A voice spoofing detection framework for IoT systems with feature pyramid and online knowledge distillation","volume":"143","author":"Ren","year":"2023","journal-title":"J. Syst. Archit."},{"key":"10.1016\/j.sigpro.2026.110668_bib0056","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.specom.2023.01.001","article-title":"Cross-modal information fusion for voice spoofing detection","volume":"147","author":"Xue","year":"2023","journal-title":"Speech. Commun."},{"key":"10.1016\/j.sigpro.2026.110668_bib0057","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126799","article-title":"Twice attention networks for synthetic speech detection","volume":"559","author":"Chen","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.sigpro.2026.110668_bib0058","article-title":"An explainable deepfake of speech detection method with spectrograms and waveforms","volume":"81","author":"Yu","year":"2024","journal-title":"J. Inf. Secur. Appl."},{"key":"10.1016\/j.sigpro.2026.110668_bib0059","doi-asserted-by":"crossref","DOI":"10.1016\/j.apacoust.2024.110047","article-title":"Dual-branch network with fused Mel features for logic-manipulated speech detection","volume":"222","author":"Yang","year":"2024","journal-title":"Appl. Acoust."},{"key":"10.1016\/j.sigpro.2026.110668_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.csl.2024.101732","article-title":"Spoofing countermeasure for fake speech detection using brute force features","volume":"90","author":"Mirza","year":"2025","journal-title":"Comput. Speech. Lang."},{"key":"10.1016\/j.sigpro.2026.110668_bib0061","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2024.109974","article-title":"A robust unified spoofing audio detection scheme","volume":"122","author":"Meng","year":"2025","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.sigpro.2026.110668_bib0062","series-title":"ICASSP 2025 \u2013 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1","article-title":"SpecViT: a custom vision-transformer based approach for audio deepfake detection","author":"Modak","year":"2025"},{"key":"10.1016\/j.sigpro.2026.110668_bib0063","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.131466","article-title":"Adaptive reverse perturbation network for audio deepfake detection","volume":"658","author":"Ouyang","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.sigpro.2026.110668_bib0064","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111413","article-title":"Deep correlation network for synthetic speech detection","volume":"154","author":"Chen","year":"2024","journal-title":"Appl. Soft. Comput."},{"key":"10.1016\/j.sigpro.2026.110668_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102257","article-title":"Multi-space channel representation learning for mono-to-binaural conversion based audio deepfake detection","volume":"105","author":"Liu","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.sigpro.2026.110668_bib0066","doi-asserted-by":"crossref","unstructured":"Guo, Y., Huang, H., Chen, X., Zhao, H., & Wang, Y. (2024). Audio deepfake detection with self-supervised WavLM and multi-fusion attentive classifier. arXiv preprint arXiv:2312.08089.","DOI":"10.1109\/ICASSP48485.2024.10447923"},{"key":"10.1016\/j.sigpro.2026.110668_bib0067","series-title":"Deepfake Audio Detection with Spectral Features and ResNeXt-Based Architecture","author":"Tahaoglu","year":"2025"},{"key":"10.1016\/j.sigpro.2026.110668_bib0068","doi-asserted-by":"crossref","first-page":"4020","DOI":"10.1109\/TASLPRO.2025.3611229","article-title":"AWaveFormer: audio wavelet transformer network for generalized audio deepfake detection","volume":"33","author":"Wang","year":"2025","journal-title":"IEEE Trans. Audio Speech. Lang. Process."}],"container-title":["Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0165168426001829?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0165168426001829?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T01:34:23Z","timestamp":1780018463000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0165168426001829"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":68,"alternative-id":["S0165168426001829"],"URL":"https:\/\/doi.org\/10.1016\/j.sigpro.2026.110668","relation":{},"ISSN":["0165-1684"],"issn-type":[{"value":"0165-1684","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep attention mechanism residual network for audio deepfake detection using multi scale cepstral coefficient features","name":"articletitle","label":"Article Title"},{"value":"Signal Processing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.sigpro.2026.110668","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"110668"}}