{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T12:13:15Z","timestamp":1775909595441,"version":"3.50.1"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"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":["Circuits Syst Signal Process"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s00034-023-02300-x","type":"journal-article","created":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T20:36:57Z","timestamp":1675975017000},"page":"4051-4071","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Nested U-Net with Efficient Channel Attention and D3Net for Speech Enhancement"],"prefix":"10.1007","volume":"42","author":[{"given":"Sivaramakrishna","family":"Yechuri","sequence":"first","affiliation":[]},{"given":"Sunnydayal","family":"Vanambathina","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"2300_CR1","first-page":"515","volume":"29","author":"BJ Borgstr\u00f6m","year":"2020","unstructured":"B.J. Borgstr\u00f6m, M.S. Brandstein, Speech enhancement via attention masking network (seamnet): An end-to-end system for joint suppression of noise and reverberation. IEEE\/ACM Trans. Audio Speech Lang. Process. 29, 515\u2013526 (2020)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"2300_CR2","volume-title":"A novel convolutional neural network model based on beetle antennae search optimization algorithm for computerized tomography diagnosis","author":"D Chen","year":"2021","unstructured":"D. Chen, X. Li, S. Li, A novel convolutional neural network model based on beetle antennae search optimization algorithm for computerized tomography diagnosis (IEEE Trans. Neural Netw. Learn, Syst, 2021)"},{"key":"2300_CR3","unstructured":"CommonVoice, Mozilla (2017). https:\/\/commonvoice.mozilla.org\/en"},{"key":"2300_CR4","first-page":"1","volume":"1","author":"X Duan","year":"2022","unstructured":"X. Duan, Y. Sun, J. Wang, Eca-unet for coronary artery segmentation and three-dimensional reconstruction. Signal Image Video Process. 1, 1\u20137 (2022)","journal-title":"Signal Image Video Process."},{"key":"2300_CR5","doi-asserted-by":"crossref","unstructured":"A. Fuchs, R. Priewald, F. Pernkopf, Recurrent dilated densenets for a time-series segmentation task, in 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, pp. 75\u201380 (2019)","DOI":"10.1109\/ICMLA.2019.00021"},{"key":"2300_CR6","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren et\u00a0al., Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2300_CR7","doi-asserted-by":"publisher","first-page":"2149","DOI":"10.1109\/LSP.2020.3040693","volume":"27","author":"TA Hsieh","year":"2020","unstructured":"T.A. Hsieh, H.M. Wang, X. Lu et al., Wavecrn: an efficient convolutional recurrent neural network for end-to-end speech enhancement. IEEE Signal Process. Lett. 27, 2149\u20132153 (2020)","journal-title":"IEEE Signal Process. Lett."},{"key":"2300_CR8","doi-asserted-by":"crossref","unstructured":"J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2300_CR9","doi-asserted-by":"crossref","unstructured":"Y. Hu, Y. Liu, S. Lv et\u00a0al., Dccrn: deep complex convolution recurrent network for phase-aware speech enhancement (2020). arXiv preprint arXiv:2008.00264","DOI":"10.21437\/Interspeech.2020-2537"},{"issue":"2","key":"2300_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-020-3073-5","volume":"65","author":"AT Khan","year":"2022","unstructured":"A.T. Khan, S. Li, X. Cao, Human guided cooperative robotic agents in smart home using beetle antennae search. Sci. China Inf. Sci. 65(2), 1\u201317 (2022)","journal-title":"Sci. China Inf. Sci."},{"key":"2300_CR11","unstructured":"D.P. Kingma, J. Ba, adam: A method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980"},{"key":"2300_CR12","doi-asserted-by":"crossref","unstructured":"S. Kumar, K. Kumar, Irsc: integrated automated review mining system using virtual machines in cloud environment, in 2018 Conference on Information and Communication Technology (CICT) (IEEE, 2018), pp 1\u20136","DOI":"10.1109\/INFOCOMTECH.2018.8722387"},{"key":"2300_CR13","doi-asserted-by":"crossref","unstructured":"S. Kumari, M. Singh, K. Kumar, Prediction of liver disease using grouping of machine learning classifiers, in International Conference on Deep Learning, Artificial Intelligence and Robotics (Springer, 2019), pp. 339\u2013349","DOI":"10.1007\/978-3-030-67187-7_35"},{"key":"2300_CR14","doi-asserted-by":"crossref","unstructured":"Y. Lei, H. Zhu, J. Zhang et\u00a0al., Meta ordinal regression forest for medical image classification with ordinal labels (2022). arXiv preprint arXiv:2203.07725","DOI":"10.1109\/JAS.2022.105668"},{"key":"2300_CR15","doi-asserted-by":"crossref","unstructured":"A. Li, C. Zheng, C. Fan et\u00a0al., A recursive network with dynamic attention for monaural speech enhancement (2020). arXiv preprint arXiv:2003.12973","DOI":"10.21437\/Interspeech.2020-1513"},{"key":"2300_CR16","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.neucom.2021.02.094","volume":"448","author":"S Li","year":"2021","unstructured":"S. Li, X. Xing, W. Fan et al., Spatiotemporal and frequential cascaded attention networks for speech emotion recognition. Neurocomputing 448, 238\u2013248 (2021)","journal-title":"Neurocomputing"},{"issue":"1","key":"2300_CR17","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/JAS.2020.1003381","volume":"8","author":"Z Li","year":"2021","unstructured":"Z. Li, S. Li, X. Luo, An overview of calibration technology of industrial robots. IEEE\/CAA J. Automatica Sinica 8(1), 23\u201336 (2021)","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"2300_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3153039","volume-title":"Diversified regularization enhanced training for effective manipulator calibration","author":"Z Li","year":"2022","unstructured":"Z. Li, S. Li, O.O. Bamasag et al., Diversified regularization enhanced training for effective manipulator calibration (IEEE Trans. Neural Netw. Learn, Syst, 2022)"},{"issue":"3","key":"2300_CR19","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1109\/TASSP.1978.1163086","volume":"26","author":"J Lim","year":"1978","unstructured":"J. Lim, A. Oppenheim, All-pole modeling of degraded speech. IEEE Trans. Acoust. Speech Signal Process. 26(3), 197\u2013210 (1978)","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"2300_CR20","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.neucom.2020.08.092","volume":"445","author":"Y Lin","year":"2021","unstructured":"Y. Lin, Q. Li, B. Yang et al., Improving speech recognition models with small samples for air traffic control systems. Neurocomputing 445, 287\u2013297 (2021)","journal-title":"Neurocomputing"},{"key":"2300_CR21","doi-asserted-by":"crossref","unstructured":"J.Y. Liu, Y.H. Yang, Dilated convolution with dilated gru for music source separation (2019). arXiv preprint arXiv:1906.01203","DOI":"10.24963\/ijcai.2019\/655"},{"key":"2300_CR22","first-page":"588","volume":"49","author":"P Loizou","year":"2017","unstructured":"P. Loizou, Y. Hu, Noizeus: a noisy speech corpus for evaluation of speech enhancement algorithms. Speech Commun. 49, 588\u2013601 (2017)","journal-title":"Speech Commun."},{"issue":"11","key":"2300_CR23","doi-asserted-by":"publisher","first-page":"5931","DOI":"10.1109\/TII.2019.2909142","volume":"15","author":"H Lu","year":"2019","unstructured":"H. Lu, L. Jin, X. Luo et al., Rnn for solving perturbed time-varying underdetermined linear system with double bound limits on residual errors and state variables. IEEE Trans. Ind. Inf. 15(11), 5931\u20135942 (2019)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"2300_CR24","doi-asserted-by":"crossref","unstructured":"A. Negi, K. Kumar, N.S. Chaudhari et\u00a0al., Predictive analytics for recognizing human activities using residual network and fine-tuning, in International Conference on Big Data Analytics (Springer, 2021), pp. 296\u2013310","DOI":"10.1007\/978-3-030-93620-4_21"},{"issue":"10","key":"2300_CR25","volume":"1","author":"A Odena","year":"2016","unstructured":"A. Odena, V. Dumoulin, C. Olah, Deconvolution and checkerboard artifacts. Distill 1(10), e3 (2016)","journal-title":"Deconvolution and checkerboard artifacts. Distill"},{"key":"2300_CR26","unstructured":"A.v.d. Oord, S. Dieleman, H. Zen et\u00a0al., Wavenet. A generative model for raw audio (2016). arXiv preprint arXiv:1609.03499"},{"key":"2300_CR27","doi-asserted-by":"crossref","unstructured":"A. Pandey, D. Wang, On adversarial training and loss functions for speech enhancement, in 2018 IEEE International Conference on Acoustics. Speech and Signal Processing (ICASSP) (IEEE, 2018), pp. 5414\u20135418","DOI":"10.1109\/ICASSP.2018.8462614"},{"issue":"7","key":"2300_CR28","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1109\/TASLP.2019.2913512","volume":"27","author":"A Pandey","year":"2019","unstructured":"A. Pandey, D. Wang, A new framework for CNN-based speech enhancement in the time domain. IEEE\/ACM Trans. Audio Speech Lang. Process. 27(7), 1179\u20131188 (2019)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"issue":"7","key":"2300_CR29","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1109\/TASLP.2019.2913512","volume":"27","author":"A Pandey","year":"2019","unstructured":"A. Pandey, D. Wang, A new framework for CNN-based speech enhancement in the time domain. IEEE\/ACM Trans. Audio Speech Lang. Process. 27(7), 1179\u20131188 (2019)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"2300_CR30","doi-asserted-by":"crossref","unstructured":"A. Pandey, D. Wang, Tcnn: temporal convolutional neural network for real-time speech enhancement in the time domain, in ICASSP 2019\u20132019 IEEE International Conference on Acoustics. Speech and Signal Processing (ICASSP) (IEEE, 2019), pp. 6875\u20136879","DOI":"10.1109\/ICASSP.2019.8683634"},{"issue":"107","key":"2300_CR31","first-page":"404","volume":"106","author":"X Qin","year":"2020","unstructured":"X. Qin, Z. Zhang, C. Huang et al., U2-net: going deeper with nested u-structure for salient object detection. Pattern Recogn. 106(107), 404 (2020)","journal-title":"Pattern Recogn."},{"key":"2300_CR32","unstructured":"Recommendation IT Perceptual evaluation of speech quality (pesq): An objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs. Rec ITU-T P 862 (2001)"},{"issue":"5","key":"2300_CR33","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1109\/TASL.2014.2315271","volume":"22","author":"V Rieser","year":"2014","unstructured":"V. Rieser, O. Lemon, S. Keizer, Natural language generation as incremental planning under uncertainty: adaptive information presentation for statistical dialogue systems. IEEE\/ACM Trans. Audio Speech Lang. Process. 22(5), 979\u2013994 (2014)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"issue":"4","key":"2300_CR34","doi-asserted-by":"publisher","first-page":"2236","DOI":"10.1121\/1.1610463","volume":"114","author":"N Roman","year":"2003","unstructured":"N. Roman, D. Wang, G.J. Brown, Speech segregation based on sound localization. J. Acoust. Soc. Am. 114(4), 2236\u20132252 (2003)","journal-title":"J. Acoust. Soc. Am."},{"key":"2300_CR35","doi-asserted-by":"crossref","unstructured":"P. Sandhya, R. Bandi, D.D. Himabindu, Stock price prediction using recurrent neural network and lstm, in 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (IEEE, 2022), pp. 1723\u20131728","DOI":"10.1109\/ICCMC53470.2022.9753764"},{"issue":"17","key":"2300_CR36","doi-asserted-by":"publisher","first-page":"26319","DOI":"10.1007\/s11042-021-10768-5","volume":"80","author":"S Sharma","year":"2021","unstructured":"S. Sharma, K. Kumar, Asl-3dcnn: American sign language recognition technique using 3-d convolutional neural networks. Multimed. Tools Appl. 80(17), 26319\u201326331 (2021)","journal-title":"Multimed. Tools Appl."},{"key":"2300_CR37","doi-asserted-by":"crossref","unstructured":"S. Sharma, S.N. Shivhare, N. Singh et\u00a0al., Computationally efficient ANN model for small-scale problems, in Machine Intelligence and Signal Analysis (Springer, 2019), pp. 423\u2013435","DOI":"10.1007\/978-981-13-0923-6_37"},{"issue":"5","key":"2300_CR38","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1007\/s11554-021-01122-x","volume":"18","author":"PN Srinivasu","year":"2021","unstructured":"P.N. Srinivasu, A.K. Bhoi, R.H. Jhaveri et al., Probabilistic deep q network for real-time path planning in censorious robotic procedures using force sensors. J. Real-Time Image Proc. 18(5), 1773\u20131785 (2021)","journal-title":"J. Real-Time Image Proc."},{"key":"2300_CR39","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3169927","volume-title":"Ambient assistive living for monitoring the physical activity of diabetic adults through body area networks","author":"PN Srinivasu","year":"2022","unstructured":"P.N. Srinivasu, G. JayaLakshmi, R.H. Jhaveri et al., Ambient assistive living for monitoring the physical activity of diabetic adults through body area networks (Mobile Inf, Syst, 2022)"},{"key":"2300_CR40","unstructured":"D. Stoller, S. Ewert, S. Dixon, Wave-u-net: a multi-scale neural network for end-to-end audio source separation (2018). arXiv preprint arXiv:1806.03185"},{"issue":"7","key":"2300_CR41","doi-asserted-by":"publisher","first-page":"2125","DOI":"10.1109\/TASL.2011.2114881","volume":"19","author":"CH Taal","year":"2011","unstructured":"C.H. Taal, R.C. Hendriks, R. Heusdens et al., An algorithm for intelligibility prediction of time-frequency weighted noisy speech. IEEE Trans. Audio Speech Lang. Process. 19(7), 2125\u20132136 (2011)","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"2300_CR42","doi-asserted-by":"crossref","unstructured":"N. Takahashi, Y. Mitsufuji, Multi-scale multi-band densenets for audio source separation, in 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (IEEE, 2017), pp. 21\u201325","DOI":"10.1109\/WASPAA.2017.8169987"},{"key":"2300_CR43","unstructured":"N. Takahashi, Y. Mitsufuji, D3net: Densely connected multidilated densenet for music source separation (2020). arXiv preprint arXiv:2010.01733"},{"key":"2300_CR44","doi-asserted-by":"crossref","unstructured":"K. Tan, D. Wang, A convolutional recurrent neural network for real-time speech enhancement, in Interspeech (2018), pp 3229\u20133233","DOI":"10.21437\/Interspeech.2018-1405"},{"key":"2300_CR45","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1109\/TASLP.2019.2955276","volume":"28","author":"K Tan","year":"2019","unstructured":"K. Tan, D. Wang, Learning complex spectral mapping with gated convolutional recurrent networks for monaural speech enhancement. IEEE\/ACM Trans. Audio Speech Lang. Process. 28, 380\u2013390 (2019)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"2300_CR46","doi-asserted-by":"publisher","first-page":"1853","DOI":"10.1109\/TASLP.2021.3082318","volume":"29","author":"K Tan","year":"2021","unstructured":"K. Tan, X. Zhang, D. Wang, Deep learning based real-time speech enhancement for dual-microphone mobile phones. IEEE\/ACM Trans. Audio Speech Lang. Process. 29, 1853\u20131863 (2021)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"2300_CR47","doi-asserted-by":"crossref","unstructured":"A. Vijayvergia, K. Kumar, Star: rating of reviews by exploiting variation in emotions using transfer learning framework, in 2018 Conference on Information and Communication Technology (CICT) (IEEE, 2018), pp. 1\u20136","DOI":"10.1109\/INFOCOMTECH.2018.8722356"},{"issue":"18","key":"2300_CR48","doi-asserted-by":"publisher","first-page":"28349","DOI":"10.1007\/s11042-021-10997-8","volume":"80","author":"A Vijayvergia","year":"2021","unstructured":"A. Vijayvergia, K. Kumar, Selective shallow models strength integration for emotion detection using glove and LSTM. Multimed. Tools Appl. 80(18), 28349\u201328363 (2021)","journal-title":"Multimed. Tools Appl."},{"key":"2300_CR49","doi-asserted-by":"publisher","DOI":"10.1109\/9780470043387","volume-title":"Computational Auditory Scene Analysis: Principles, Algorithms, and Applications","author":"D Wang","year":"2006","unstructured":"D. Wang, G.J. Brown, Computational Auditory Scene Analysis: Principles, Algorithms, and Applications (Wiley, New York, 2006)"},{"key":"2300_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3216413","volume":"71","author":"H Wang","year":"2022","unstructured":"H. Wang, T. Lin, L. Cui et al., Multitask learning-based self-attention encoding atrous convolutional neural network for remaining useful life prediction. IEEE Trans. Instrum. Meas. 71, 1\u20138 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"2300_CR51","doi-asserted-by":"crossref","unstructured":"Q. Wang, B. Wu, P. Zhu, et\u00a0al, Supplementary material for \u2018eca-net: Efficient channel attention for deep convolutional neural networks, in Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (IEEE, Seattle, WA, USA, 2020), pp. 13\u201319","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"2300_CR52","first-page":"1","volume":"19","author":"W Wang","year":"2022","unstructured":"W. Wang, C. Tang, X. Wang et al., A vit-based multiscale feature fusion approach for remote sensing image segmentation. IEEE Geosci. Rem. Sens. Lett. 19, 1\u20135 (2022)","journal-title":"IEEE Geosci. Rem. Sens. Lett."},{"issue":"12","key":"2300_CR53","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.1109\/TASLP.2014.2352935","volume":"22","author":"Y Wang","year":"2014","unstructured":"Y. Wang, A. Narayanan, D. Wang, On training targets for supervised speech separation. IEEE\/ACM Trans. Audio Speech Lang. Process. 22(12), 1849\u20131858 (2014)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"issue":"5","key":"2300_CR54","doi-asserted-by":"publisher","first-page":"2400","DOI":"10.1007\/s00034-020-01577-6","volume":"40","author":"P Wen","year":"2021","unstructured":"P. Wen, J. Zhang, S. Zhang et al., Normalized subband spline adaptive filter: algorithm derivation and analysis. Circuits Syst. Signal Process. 40(5), 2400\u20132418 (2021)","journal-title":"Circuits Syst. Signal Process."},{"key":"2300_CR55","doi-asserted-by":"crossref","unstructured":"P. Wen, B. Wang, S. Zhang, et\u00a0al., Bias-compensated augmented complex-valued nsaf algorithm and its low-complexity implementation. Signal Process. 108812 (2022)","DOI":"10.1016\/j.sigpro.2022.108812"},{"issue":"1","key":"2300_CR56","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/JSTSP.2020.3045846","volume":"15","author":"Y Xian","year":"2020","unstructured":"Y. Xian, Y. Sun, W. Wang et al., A multi-scale feature recalibration network for end-to-end single channel speech enhancement. IEEE J. Sel. Top. Signal Process. 15(1), 143\u2013155 (2020)","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"2300_CR57","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1109\/LSP.2021.3093859","volume":"28","author":"X Xiang","year":"2021","unstructured":"X. Xiang, X. Zhang, H. Chen, A convolutional network with multi-scale and attention mechanisms for end-to-end single-channel speech enhancement. IEEE Signal Process. Lett. 28, 1455\u20131459 (2021)","journal-title":"IEEE Signal Process. Lett."},{"key":"2300_CR58","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1109\/LSP.2021.3128374","volume":"29","author":"X Xiang","year":"2021","unstructured":"X. Xiang, X. Zhang, H. Chen, A nested u-net with self-attention and dense connectivity for monaural speech enhancement. IEEE Signal Process. Lett. 29, 105\u2013109 (2021)","journal-title":"IEEE Signal Process. Lett."},{"key":"2300_CR59","first-page":"9633","volume":"33","author":"R Xu","year":"2020","unstructured":"R. Xu, R. Wu, Y. Ishiwaka et al., Listening to sounds of silence for speech denoising. Adv. Neural. Inf. Process. Syst. 33, 9633\u20139648 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"2300_CR60","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1109\/TASLP.2014.2364452","volume":"23","author":"Y Xu","year":"2014","unstructured":"Y. Xu, J. Du, L.R. Dai et al., A regression approach to speech enhancement based on deep neural networks. IEEE\/ACM Trans. Audio Speech Lang. Process. 23(1), 7\u201319 (2014)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"issue":"6","key":"2300_CR61","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1109\/LSP.2005.847864","volume":"12","author":"K Yamashita","year":"2005","unstructured":"K. Yamashita, T. Shimamura, Nonstationary noise estimation using low-frequency regions for spectral subtraction. IEEE Signal Process. Lett. 12(6), 465\u2013468 (2005)","journal-title":"IEEE Signal Process. Lett."},{"key":"2300_CR62","first-page":"1","volume":"60","author":"X Yang","year":"2022","unstructured":"X. Yang, J. Zhang, C. Chen et al., An efficient and lightweight CNN model with soft quantification for ship detection in SAR images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201313 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"6","key":"2300_CR63","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1109\/LSP.2005.845594","volume":"12","author":"CH You","year":"2005","unstructured":"C.H. You, S.N. Koh, S. Rahardja, An invertible frequency eigendomain transformation for masking-based subspace speech enhancement. IEEE Signal Process. Lett. 12(6), 461\u2013464 (2005)","journal-title":"IEEE Signal Process. Lett."},{"key":"2300_CR64","doi-asserted-by":"publisher","first-page":"1404","DOI":"10.1109\/TASLP.2020.2987441","volume":"28","author":"Q Zhang","year":"2020","unstructured":"Q. Zhang, A. Nicolson, M. Wang et al., Deepmmse: A deep learning approach to mmse-based noise power spectral density estimation. IEEE\/ACM Trans. Audio Speech Lang. Process. 28, 1404\u20131415 (2020)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"2300_CR65","doi-asserted-by":"crossref","unstructured":"C. Zheng, X. Peng, Y. Zhang et\u00a0al., Interactive speech and noise modeling for speech enhancement, in Proceedings of the AAAI Conference on Artificial Intelligence (2021), pp. 14549\u201314557","DOI":"10.1609\/aaai.v35i16.17710"}],"container-title":["Circuits, Systems, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-023-02300-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00034-023-02300-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-023-02300-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T05:07:03Z","timestamp":1687324023000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00034-023-02300-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,9]]},"references-count":65,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["2300"],"URL":"https:\/\/doi.org\/10.1007\/s00034-023-02300-x","relation":{},"ISSN":["0278-081X","1531-5878"],"issn-type":[{"value":"0278-081X","type":"print"},{"value":"1531-5878","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,9]]},"assertion":[{"value":"30 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interests"}}]}}