Regression-Based Noise Modeling for Speech Signal Processing

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Autor(es): dc.contributorUNEMAT - Mato Grosso State University-
Autor(es): dc.contributorUniversidade Estadual de Mato Grosso do Sul (UEMS)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorAbreu, Caio Cesar Enside de-
Autor(es): dc.creatorDuarte, Marco Aparecido Queiroz-
Autor(es): dc.creatorOliveira, Bruno Rodrigues de [UNESP]-
Autor(es): dc.creatorFilho, Jozue Vieira [UNESP]-
Autor(es): dc.creatorVillarreal, Francisco [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:53:17Z-
Data de disponibilização: dc.date.available2022-02-22T00:53:17Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1142/S021947752150022X-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/208384-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/208384-
Descrição: dc.descriptionSpeech processing systems are very important in different applications involving speech and voice quality such as automatic speech recognition, forensic phonetics and speech enhancement, among others. In most of them, the acoustic environmental noise is added to the original signal, decreasing the signal-to-noise ratio (SNR) and the speech quality by consequence. Therefore, estimating noise is one of the most important steps in speech processing whether to reduce it before processing or to design robust algorithms. In this paper, a new approach to estimate noise from speech signals is presented and its effectiveness is tested in the speech enhancement context. For this purpose, partial least squares (PLS) regression is used to model the acoustic environment (AE) and a Wiener filter based on a priori SNR estimation is implemented to evaluate the proposed approach. Six noise types are used to create seven acoustically modeled noises. The basic idea is to consider the AE model to identify the noise type and estimate its power to be used in a speech processing system. Speech signals processed using the proposed method and classical noise estimators are evaluated through objective measures. Results show that the proposed method produces better speech quality than state-of-the-art noise estimators, enabling it to be used in real-time applications in the field of robotic, telecommunications and acoustic analysis.-
Descrição: dc.descriptionDepartment of Computing UNEMAT - Mato Grosso State University-
Descrição: dc.descriptionDepartment of Mathematics UEMS - Mato Grosso do Sul State University-
Descrição: dc.descriptionDepartment of Electrical Engineering UNESP - São Paulo State University-
Descrição: dc.descriptionTelecommunications and Aeronautical Engineering UNESP - São Paulo State University-
Descrição: dc.descriptionDepartment of Mathematics UNESP - São Paulo State University-
Descrição: dc.descriptionDepartment of Electrical Engineering UNESP - São Paulo State University-
Descrição: dc.descriptionTelecommunications and Aeronautical Engineering UNESP - São Paulo State University-
Descrição: dc.descriptionDepartment of Mathematics UNESP - São Paulo State University-
Idioma: dc.languageen-
Relação: dc.relationFluctuation and Noise Letters-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectnoise classification-
Palavras-chave: dc.subjectnoise estimation-
Palavras-chave: dc.subjectpartial least squares-
Palavras-chave: dc.subjectSpeech processing-
Título: dc.titleRegression-Based Noise Modeling for Speech Signal Processing-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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