Model predictive PESQ-ANFIS/FUZZY C-MEANS for image-based speech signal evaluation

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorMato Grosso State University (UNEMAT)-
Autor(es): dc.contributorPantanal Editora-
Autor(es): dc.creatorNeves, Eder Pereira-
Autor(es): dc.creatorDuarte, Marco Aparecido Queiroz-
Autor(es): dc.creatorFilho, Jozue Vieira-
Autor(es): dc.creatorde Abreu, Caio Cesar Enside-
Autor(es): dc.creatorde Oliveira, Bruno Rodrigues-
Data de aceite: dc.date.accessioned2025-08-21T19:30:23Z-
Data de disponibilização: dc.date.available2025-08-21T19:30:23Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-10-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.specom.2023.102972-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/309502-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/309502-
Descrição: dc.descriptionThis paper presents a new method to evaluate the quality of speech signals through images generated from a psychoacoustic model to estimate PESQ (ITU-T P862) values using a first-order Fuzzy Sugeno approach implemented in the Adaptive Neuro-Fuzzy Inference System - ANFIS. The factors feeding the network were obtained using an image-processing technique from the perceptual model coefficients. All simulations were performed using a database containing clean and corrupted signals by eight types of noises found in everyday situations. The proposal uses the PESQ values of the signals to train the network. The analyses proved that the predictive performance will depend on the choice of a psychoacoustic model, the factor extraction technique, the combination of these factors, the fuzzification algorithm, and the type of membership function in the ANFIS input space. The data sets for training and testing for each signal directory were randomly created and executed fifty times. The proposal achieves the best prediction values for PESQ when the averages of the measurements reach MAPE ≤0.09, RMSE ≤0.20, and R2≥95. In general, the approach provided satisfactory results compared to Multilayer Perceptron networks with their different learning algorithms, compared to another psychoacoustic model, to ITU-T P.563 and other non-intrusive methods that evaluate the quality of voice signals, and it was efficient regardless of the number of signals and the database used.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionDepartment of Mathematics Mato Grosso do Sul State University (UEMS)-
Descrição: dc.descriptionDepartment of Electrical Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionTelecommunication and Aeronautic Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of Computing Mato Grosso State University (UNEMAT)-
Descrição: dc.descriptionPantanal Editora, Rua Abaete, 83, Sala B, Centro. 78., MT-
Descrição: dc.descriptionDepartment of Electrical Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionTelecommunication and Aeronautic Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionCAPES: 001-
Idioma: dc.languageen-
Relação: dc.relationSpeech Communication-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectFactor extraction-
Palavras-chave: dc.subjectFuzzification-
Palavras-chave: dc.subjectPerceptual imaging-
Palavras-chave: dc.subjectPESQ estimate-
Título: dc.titleModel predictive PESQ-ANFIS/FUZZY C-MEANS for image-based speech signal evaluation-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.