The Haar Wavelet Transform in IoT Digital Audio Signal Processing
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s00034-022-01979-8 http://hdl.handle.net/11449/234150 |
Resumo: | Digital signal processing allows a wide range of digital applications, including audio processing. Discrete wavelet transform (DWT) is one of the most efficient ways of time–frequency analysis of an audio signal. The Internet of Things (IoT) is a technology that has been growing in commercial automation applications with the availability of low-cost modules and microcontrollers. IoT projects that use DWT for digital audio processing can solve many problems involving audio signals. In this paper, a performance comparison of an algorithm implementing DWT on three commercially available IoT devices is presented. The results show that it is possible to process signals in the 256 Hz to 6.5 KHz range, opening possibilities for future work integrating the technologies described. |
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Repositório Institucional da UNESP |
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The Haar Wavelet Transform in IoT Digital Audio Signal ProcessingDigital Signal ProcessingInternet of ThingsPerformance analysisWaveletsDigital signal processing allows a wide range of digital applications, including audio processing. Discrete wavelet transform (DWT) is one of the most efficient ways of time–frequency analysis of an audio signal. The Internet of Things (IoT) is a technology that has been growing in commercial automation applications with the availability of low-cost modules and microcontrollers. IoT projects that use DWT for digital audio processing can solve many problems involving audio signals. In this paper, a performance comparison of an algorithm implementing DWT on three commercially available IoT devices is presented. The results show that it is possible to process signals in the 256 Hz to 6.5 KHz range, opening possibilities for future work integrating the technologies described.Instituto Federal de São Paulo, Av. C1 n250, SPInstituto Federal de Goiás, Rua 75 n46, GOInstituto de Biociências Letras e Ciências Exatas Unesp - Universidade Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo n2265, SPEscola de Engenharia de São Carlos Universidade de São Paulo, Av. Trabalhador São-carlense n400, SPEMC Universidade Federal de Goiás, Av. Universitária n1488, GOInstituto de Biociências Letras e Ciências Exatas Unesp - Universidade Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo n2265, SPInstituto Federal de São PauloInstituto Federal de GoiásUniversidade Estadual Paulista (UNESP)Universidade de São Paulo (USP)Universidade Federal de Goiás (UFG)Escola, João Paulo Lemosde Souza, Uender BarbosaGuido, Rodrigo Capobianco [UNESP]Silva, Ivan Nunes da2022-05-01T13:41:38Z2022-05-01T13:41:38Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s00034-022-01979-8Circuits, Systems, and Signal Processing.1531-58780278-081Xhttp://hdl.handle.net/11449/23415010.1007/s00034-022-01979-82-s2.0-85124730665Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengCircuits, Systems, and Signal Processinginfo:eu-repo/semantics/openAccess2022-05-01T13:41:38Zoai:repositorio.unesp.br:11449/234150Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:15:00.465726Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
The Haar Wavelet Transform in IoT Digital Audio Signal Processing |
title |
The Haar Wavelet Transform in IoT Digital Audio Signal Processing |
spellingShingle |
The Haar Wavelet Transform in IoT Digital Audio Signal Processing Escola, João Paulo Lemos Digital Signal Processing Internet of Things Performance analysis Wavelets |
title_short |
The Haar Wavelet Transform in IoT Digital Audio Signal Processing |
title_full |
The Haar Wavelet Transform in IoT Digital Audio Signal Processing |
title_fullStr |
The Haar Wavelet Transform in IoT Digital Audio Signal Processing |
title_full_unstemmed |
The Haar Wavelet Transform in IoT Digital Audio Signal Processing |
title_sort |
The Haar Wavelet Transform in IoT Digital Audio Signal Processing |
author |
Escola, João Paulo Lemos |
author_facet |
Escola, João Paulo Lemos de Souza, Uender Barbosa Guido, Rodrigo Capobianco [UNESP] Silva, Ivan Nunes da |
author_role |
author |
author2 |
de Souza, Uender Barbosa Guido, Rodrigo Capobianco [UNESP] Silva, Ivan Nunes da |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Instituto Federal de São Paulo Instituto Federal de Goiás Universidade Estadual Paulista (UNESP) Universidade de São Paulo (USP) Universidade Federal de Goiás (UFG) |
dc.contributor.author.fl_str_mv |
Escola, João Paulo Lemos de Souza, Uender Barbosa Guido, Rodrigo Capobianco [UNESP] Silva, Ivan Nunes da |
dc.subject.por.fl_str_mv |
Digital Signal Processing Internet of Things Performance analysis Wavelets |
topic |
Digital Signal Processing Internet of Things Performance analysis Wavelets |
description |
Digital signal processing allows a wide range of digital applications, including audio processing. Discrete wavelet transform (DWT) is one of the most efficient ways of time–frequency analysis of an audio signal. The Internet of Things (IoT) is a technology that has been growing in commercial automation applications with the availability of low-cost modules and microcontrollers. IoT projects that use DWT for digital audio processing can solve many problems involving audio signals. In this paper, a performance comparison of an algorithm implementing DWT on three commercially available IoT devices is presented. The results show that it is possible to process signals in the 256 Hz to 6.5 KHz range, opening possibilities for future work integrating the technologies described. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-05-01T13:41:38Z 2022-05-01T13:41:38Z 2022-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s00034-022-01979-8 Circuits, Systems, and Signal Processing. 1531-5878 0278-081X http://hdl.handle.net/11449/234150 10.1007/s00034-022-01979-8 2-s2.0-85124730665 |
url |
http://dx.doi.org/10.1007/s00034-022-01979-8 http://hdl.handle.net/11449/234150 |
identifier_str_mv |
Circuits, Systems, and Signal Processing. 1531-5878 0278-081X 10.1007/s00034-022-01979-8 2-s2.0-85124730665 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Circuits, Systems, and Signal Processing |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128486637830144 |