000120242 001__ 120242
000120242 005__ 20240319081026.0
000120242 0247_ $$2doi$$a10.3390/app12189000
000120242 0248_ $$2sideral$$a131193
000120242 037__ $$aART-2022-131193
000120242 041__ $$aeng
000120242 100__ $$0(orcid)0000-0003-3813-4998$$aRibas, Dayana
000120242 245__ $$aWiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement
000120242 260__ $$c2022
000120242 5060_ $$aAccess copy available to the general public$$fUnrestricted
000120242 5203_ $$aThis paper proposes a Deep Learning (DL) based Wiener filter estimator for speech enhancement in the framework of the classical spectral-domain speech estimator algorithm. According to the characteristics of the intermediate steps of the speech enhancement algorithm, i.e., the SNR estimation and the gain function, there is determined the best usage of the network at learning a robust instance of the Wiener filter estimator. Experiments show that the use of data-driven learning of the SNR estimator provides robustness to the statistical-based speech estimator algorithm and achieves performance on the state-of-the-art. Several objective quality metrics show the performance of the speech enhancement and beyond them, there are examples of noisy vs. enhanced speech available for listening to demonstrate in practice the skills of the method in simulated and real audio.
000120242 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PDC2021-120846-C41$$9info:eu-repo/grantAgreement/ES/AEI/PID2021-126061OB-C44$$9info:eu-repo/grantAgreement/ES/DGA/T36-20R$$9info:eu-repo/grantAgreement/EC/H2020/101007666/EU/Exchanges for SPEech ReseArch aNd TechnOlogies/ESPERANTO$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101007666-ESPERANTO$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/10.13039/501100011033
000120242 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000120242 590__ $$a2.7$$b2022
000120242 592__ $$a0.492$$b2022
000120242 591__ $$aPHYSICS, APPLIED$$b78 / 160 = 0.488$$c2022$$dQ2$$eT2
000120242 591__ $$aENGINEERING, MULTIDISCIPLINARY$$b42 / 90 = 0.467$$c2022$$dQ2$$eT2
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000120242 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b208 / 343 = 0.606$$c2022$$dQ3$$eT2
000120242 593__ $$aFluid Flow and Transfer Processes$$c2022$$dQ2
000120242 593__ $$aMaterials Science (miscellaneous)$$c2022$$dQ2
000120242 593__ $$aEngineering (miscellaneous)$$c2022$$dQ2
000120242 593__ $$aInstrumentation$$c2022$$dQ2
000120242 593__ $$aProcess Chemistry and Technology$$c2022$$dQ3
000120242 593__ $$aComputer Science Applications$$c2022$$dQ3
000120242 594__ $$a4.5$$b2022
000120242 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000120242 700__ $$0(orcid)0000-0001-5803-4316$$aMiguel, Antonio$$uUniversidad de Zaragoza
000120242 700__ $$0(orcid)0000-0002-3886-7748$$aOrtega, Alfonso$$uUniversidad de Zaragoza
000120242 700__ $$0(orcid)0000-0001-9137-4013$$aLleida, Eduardo$$uUniversidad de Zaragoza
000120242 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000120242 773__ $$g12, 18 (2022), 9000 [14 pp.]$$pAppl. sci.$$tApplied Sciences (Switzerland)$$x2076-3417
000120242 8564_ $$s3526773$$uhttps://zaguan.unizar.es/record/120242/files/texto_completo.pdf$$yVersión publicada
000120242 8564_ $$s2757637$$uhttps://zaguan.unizar.es/record/120242/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
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000120242 951__ $$a2024-03-18-16:43:15
000120242 980__ $$aARTICLE