120242 20240319081026.0 doi 10.3390/app12189000 sideral 131193 ART-2022-131193 eng Ribas, Dayana (orcid)0000-0003-3813-4998 Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement 2022 Access copy available to the general public Unrestricted This 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. info:eu-repo/grantAgreement/ES/AEI/PDC2021-120846-C41 info:eu-repo/grantAgreement/ES/AEI/PID2021-126061OB-C44 info:eu-repo/grantAgreement/ES/DGA/T36-20R info:eu-repo/grantAgreement/EC/H2020/101007666/EU/Exchanges for SPEech ReseArch aNd TechnOlogies/ESPERANTO This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101007666-ESPERANTO info:eu-repo/grantAgreement/ES/MICINN-AEI/10.13039/501100011033 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.7 2022 PHYSICS, APPLIED 78 / 160 = 0.488 2022 Q2 T2 ENGINEERING, MULTIDISCIPLINARY 42 / 90 = 0.467 2022 Q2 T2 CHEMISTRY, MULTIDISCIPLINARY 100 / 178 = 0.562 2022 Q3 T2 MATERIALS SCIENCE, MULTIDISCIPLINARY 208 / 343 = 0.606 2022 Q3 T2 0.492 2022 Fluid Flow and Transfer Processes 2022 Q2 Materials Science (miscellaneous) 2022 Q2 Engineering (miscellaneous) 2022 Q2 Instrumentation 2022 Q2 Process Chemistry and Technology 2022 Q3 Computer Science Applications 2022 Q3 4.5 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Miguel, Antonio Universidad de Zaragoza (orcid)0000-0001-5803-4316 Ortega, Alfonso Universidad de Zaragoza (orcid)0000-0002-3886-7748 Lleida, Eduardo Universidad de Zaragoza (orcid)0000-0001-9137-4013 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 12, 18 (2022), 9000 [14 pp.] Appl. sci. Applied Sciences (Switzerland) 2076-3417 3526773 http://zaguan.unizar.es/record/120242/files/texto_completo.pdf Versión publicada 2757637 http://zaguan.unizar.es/record/120242/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:120242 articulos driver 2024-03-18-16:43:15 ARTICLE