Progressive loss functions for speech enhancement with deep neural networks

Llombart, J. ; Ribas, D. (Universidad de Zaragoza) ; Miguel, A. (Universidad de Zaragoza) ; Vicente, L. (Universidad de Zaragoza) ; Ortega, A. (Universidad de Zaragoza) ; Lleida, E. (Universidad de Zaragoza)
Progressive loss functions for speech enhancement with deep neural networks
Resumen: The 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.
Idioma: Inglés
DOI: 10.1186/s13636-020-00191-3
Año: 2021
Publicado en: EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING 2021, 1 (2021), 1 [16 pp]
ISSN: 1687-4714

Factor impacto JCR: 2.114 (2021)
Categ. JCR: ACOUSTICS rank: 16 / 32 = 0.5 (2021) - Q2 - T2
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 169 / 277 = 0.61 (2021) - Q3 - T2

Factor impacto CITESCORE: 2.9 - Engineering (Q2) - Physics and Astronomy (Q3)

Factor impacto SCIMAGO: 0.485 - Electrical and Electronic Engineering (Q2) - Acoustics and Ultrasonics (Q2)

Tipo y forma: Article (Published version)
Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)

Creative Commons You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

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 Record created 2021-02-16, last modified 2023-05-19

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