000099123 001__ 99123
000099123 005__ 20230519145358.0
000099123 0247_ $$2doi$$a10.1186/s13636-020-00191-3
000099123 0248_ $$2sideral$$a122560
000099123 037__ $$aART-2021-122560
000099123 041__ $$aeng
000099123 100__ $$0(orcid)0000-0002-9407-5817$$aLlombart, J.
000099123 245__ $$aProgressive loss functions for speech enhancement with deep neural networks
000099123 260__ $$c2021
000099123 5060_ $$aAccess copy available to the general public$$fUnrestricted
000099123 5203_ $$aThe progressive paradigm is a promising strategy to optimize network performance for speech enhancement purposes. Recent works have shown different strategies to improve the accuracy of speech enhancement solutions based on this mechanism. This paper studies the progressive speech enhancement using convolutional and residual neural network architectures and explores two criteria for loss function optimization: weighted and uniform progressive. This work carries out the evaluation on simulated and real speech samples with reverberation and added noise using REVERB and VoiceHome datasets. Experimental results show a variety of achievements among the loss function optimization criteria and the network architectures. Results show that the progressive design strengthens the model and increases the robustness to distortions due to reverberation and noise.
000099123 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000099123 590__ $$a2.114$$b2021
000099123 592__ $$a0.485$$b2021
000099123 594__ $$a2.9$$b2021
000099123 591__ $$aACOUSTICS$$b16 / 32 = 0.5$$c2021$$dQ2$$eT2
000099123 593__ $$aElectrical and Electronic Engineering$$c2021$$dQ2
000099123 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b169 / 277 = 0.61$$c2021$$dQ3$$eT2
000099123 593__ $$aAcoustics and Ultrasonics$$c2021$$dQ2
000099123 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000099123 700__ $$0(orcid)0000-0003-3813-4998$$aRibas, D.$$uUniversidad de Zaragoza
000099123 700__ $$0(orcid)0000-0001-5803-4316$$aMiguel, A.$$uUniversidad de Zaragoza
000099123 700__ $$0(orcid)0000-0003-4391-5203$$aVicente, L.$$uUniversidad de Zaragoza
000099123 700__ $$0(orcid)0000-0002-3886-7748$$aOrtega, A.$$uUniversidad de Zaragoza
000099123 700__ $$0(orcid)0000-0001-9137-4013$$aLleida, E.$$uUniversidad de Zaragoza
000099123 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000099123 773__ $$g2021, 1 (2021), 1 [16 pp]$$pEURASIP j. audio, speech music. process.$$tEURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING$$x1687-4714
000099123 8564_ $$s971268$$uhttps://zaguan.unizar.es/record/99123/files/texto_completo.pdf$$yVersión publicada
000099123 8564_ $$s2751366$$uhttps://zaguan.unizar.es/record/99123/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000099123 909CO $$ooai:zaguan.unizar.es:99123$$particulos$$pdriver
000099123 951__ $$a2023-05-18-13:35:27
000099123 980__ $$aARTICLE